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RT10xx image reserve the APP FCB methods 1. Abstract     Regarding RT10XX programming, it is mainly divided into two categories: 1) Serial download mode with blhost proramming     To this method, we can use the MCUBootUtility tool, or blhost+elftosb+sdphost cmd method, we also can use the NXP SPT(MCUXpresso secure provisional Tool). This programming need to enter the serial download mode, then use the flashloader supported UART or the USB HID interface. 2) Use Programmer or debugger with flashdriver programming This method is usually through the SWD/JTAG download interface combined with the debugger + IDE, or directly software burning, the chip mode can be in the internal boot, or in the serial download mode, with the help of the flashloader to generate the flash burning algorithm file. Method 2, The burning method using the debugger tool usually ensures that the burning code is consistent with the original APP.     Method 1, Uses the blhost method to download, usually blhost will regenerate an FCB with a full-featured LUT to burn to the external flash, and then burn the app code with IVT, that is, without the FCB header of the original APP, and re-assemble a blhost generated FCB header and burn it separately. However, for some customers who need to read out the flash image and compare with the original APP image to check the difference after burning, the commonly used blhost method will have the problem of inconsistent FCB area matching. If the customer needs to use the blhost burning method in serial download mode, how to ensure that the flash image after burning is consistent with the original burning file? This article will take the MIMXRT1060-EVK development board as an example, and give specific methods for the command mode and SPT tool mode. 2 Blhost programming reserve APP FCB     From the old RT1060 SDK FCB file (below SDK2.12.0), evkmimxrt1060_flexspi_nor_config.c, we can see:   const flexspi_nor_config_t qspiflash_config = { .memConfig = { .tag = FLEXSPI_CFG_BLK_TAG, .version = FLEXSPI_CFG_BLK_VERSION, .readSampleClksrc=kFlexSPIReadSampleClk_LoopbackFromDqsPad, .csHoldTime = 3u, .csSetupTime = 3u, .sflashPadType = kSerialFlash_4Pads, .serialClkFreq = kFlexSpiSerialClk_100MHz, .sflashA1Size = 8u * 1024u * 1024u, .lookupTable = { // Read LUTs FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0xEB, RADDR_SDR, FLEXSPI_4PAD, 0x18), FLEXSPI_LUT_SEQ(DUMMY_SDR, FLEXSPI_4PAD, 0x06, READ_SDR, FLEXSPI_4PAD, 0x04), }, }, .pageSize = 256u, .sectorSize = 4u * 1024u, .blockSize = 64u * 1024u, .isUniformBlockSize = false, };   This FCB LUT just contains the basic read command, normally, to the app booting, the FCB just need to provide the read command to the ROM, then it can boot normally.     But what happens to the memory downloaded by blhost? Based on the MIMXRT1060-EVK development board, the following shows how to use the command line mode corresponding to blhost to burn the SDK led_blinky project app, and read out the corresponding flash burning code to analysis. 2.1 Normal blhost download command line    This command line also the same as MCUBootUtility download log, source code is attached rt1060 cmd.bat. elftosb.exe -f imx -V -c imx_application_gen.bd -o ivt_evkmimxrt1060_iled_blinky_FCB.bin evkmimxrt1060_iled_blinky.s19 sdphost.exe -t 50000 -u 0x1FC9,0x0135 -j -- write-file 0x20208200 ivt_flashloader.bin sdphost.exe -t 50000 -u 0x1FC9,0x0135 -j -- jump-address 0x20208200 blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- get-property 1 0 blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- get-property 24 0 blhost.exe -t 5242000 -u 0x15A2,0x0073 -j -- fill-memory 0x20202000 4 0xc0000007 word  //option 0 blhost.exe -t 5242000 -u 0x15A2,0x0073 -j -- fill-memory 0x20202004 4 0 word                 //option1 blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- configure-memory 9 0x20202000                    blhost -t 2048000 -u 0x15A2,0x0073 -j -- flash-erase-region 0x60000000 0x8000 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- fill-memory 0x20203000 4 0XF000000F word  blhost -t 50000 -u 0x15A2,0x0073 -j -- configure-memory 9 0x20203000                    blhost -t 5242000 -u 0x15A2,0x0073 -j -- write-memory 0x60001000 ivt_evkmimxrt1060_iled_blinky_FCB_nopadding.bin 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- read-memory 0x60000000 0x8000 flexspiNorCfg.dat 9 The normal blhost programming is to use the cmd line method, and provide an app which is without the FCB header(Even app with the FCB, will exclude the FCB header at first), then use the elftosb.exe generate the app with IVT, eg ivt_evkmimxrt1060_iled_blinky_FCB_nopadding.bin, download the flashloader file ivt_flashloader to internal RAM, and jump to the flashloader, then use the fill-memory to fill option0, option1 to choose the proper external flash, and use the configure-memory to configure the flexSPI module, with the SFDP table which is got from get configure command, then fill the flexSPI LUT internal buffer. Next, fill-memory 0x20203000 4 0XF000000F associate with configure-memory will generate the full FCB header, burn it from flash address 0x60000000. At last, burn the app which contains IVT from flash address 0X60001000, until now, realize the whole app image programming. Pic 1 shows the comparison between the data read after programming and the original app data. It can be seen that the LUT of the FCB actually programmed on the left is not only contains read, but also contains read status, write enable, program and erase commands. The one on the right is the original app with FCB. The LUT of FCB only contains read commands for boot. So, if you want to keep the FCB header of the original APP instead of the header generated and burned by option0,1 configure-memory, how to do it? The method is that you can also use Option0, 1 to generate and fill in the LUT for flexSPI for communication use, but do not burn the corresponding generated FCB, just burn the FCB that comes with the original APP. pic1 2.2 Reuse option0 and option1 to program the original APP LUT The following command gives reuse option0 and option1, generates LUT and fills in flexSPI LUT for connection with external flash interface, but does not call:  fill-memory 0x20203000 4 0XF000000F and configure-memory 9 0x20203000, so that the generated FCB will not be burned to external memory.    Source file is attached rt1060 cmd_option01.bat. elftosb.exe -f imx -V -c imx_application_gen.bd -o ivt_evkmimxrt1060_iled_blinky_FCB.bin evkmimxrt1060_iled_blinky.s19 sdphost.exe -t 50000 -u 0x1FC9,0x0135 -j -- write-file 0x20208200 ivt_flashloader.bin sdphost.exe -t 50000 -u 0x1FC9,0x0135 -j -- jump-address 0x20208200 blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- get-property 1 0 blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- get-property 24 0 blhost.exe -t 5242000 -u 0x15A2,0x0073 -j -- fill-memory 0x20202000 4 0xc0000007 word blhost.exe -t 5242000 -u 0x15A2,0x0073 -j -- fill-memory 0x20202004 4 0 word blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- configure-memory 9 0x20202000 blhost -t 5242000 -u 0x15A2,0x0073 -j -- read-memory 0x60000000 1024 flexspiNorCfg.dat 9 blhost -t 2048000 -u 0x15A2,0x0073 -j -- flash-erase-region 0x60000000 0x8000 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- read-memory 0x60000000 1024 flexspiNorCfg.dat 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- write-memory 0x60000000 evkmimxrt1060_iled_blinky_FCB.bin 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- read-memory 0x60000000 0x8000 flexspiNorCfg.dat 9 Pic 2 is the comparison between the read data after programming and the original programming data. It can be seen that the FCB programmed at this time is exactly the same as the original code FCB. Pic 2 2.3 use 1bit FCB file to configure LUT    The used file cfg_fdcb_RTxxx_1bit_sdr_flashA.bin is copied from MCUBOOTUtility: \NXP-MCUBootUtility-3.4.0\src\targets\fdcb_model . The configuration of Option0 and Option1 is usually for chips that can support SFDP table, but some flash chips cannot support SFDP table. At this time, you need to fill in the flexSPI LUT for the full LUT manually. The so-called full LUT command is not only read commands, but also supports erasing, program, etc. In this way, the flexSPI interface can be successfully connected to the external FLASH, and the corresponding functions of reading, erasing, and writing can be realized. Therefore, the method in this chapter is to use a single-line command, which is also a command supported by general chips, to enable the corresponding function of flexSPI, so it can complete the subsequent APP code programming.   Pic 3     We can see: 03H is read, 05H is read status register, 06H is write enable, D8H is the block 64K erase, 02H is the page program, 60H is the chip erase. This is the 1bit SPI method full function LUT command, which can realize the chip read, write and erase function.     The command line is, source file is attached rt1060 cmd_fdcb_1bit_sdr_flashA.bat: elftosb.exe -f imx -V -c imx_application_gen.bd -o ivt_evkmimxrt1060_iled_blinky_FCB.bin evkmimxrt1060_iled_blinky.s19 sdphost.exe -t 50000 -u 0x1FC9,0x0135 -j -- write-file 0x20208200 ivt_flashloader.bin sdphost.exe -t 50000 -u 0x1FC9,0x0135 -j -- jump-address 0x20208200 blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- get-property 1 0 blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- get-property 24 0 blhost -t 5242000 -u 0x15A2,0x0073 -j -- write-memory 0x20202000 cfg_fdcb_RTxxx_1bit_sdr_flashA.bin blhost.exe -t 50000 -u 0x15A2,0x0073 -j -- configure-memory 9 0x20202000 blhost -t 5242000 -u 0x15A2,0x0073 -j -- read-memory 0x60000000 1024 flexspiNorCfg.dat 9 blhost -t 2048000 -u 0x15A2,0x0073 -j -- flash-erase-region 0x60000000 0x8000 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- read-memory 0x60000000 1024 flexspiNorCfg.dat 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- write-memory 0x60000000 evkmimxrt1060_iled_blinky_FCB.bin 9 blhost -t 5242000 -u 0x15A2,0x0073 -j -- read-memory 0x60000000 0x8000 flexspiNorCfg.dat 9 In the command line, where option0,1 was previously filled in, instead of filling in the data of option0,1, the 512-byte Bin file of the complete FCB LUT command is directly given, and then the configure-memory command is used to configure the flashloader’s FlexSPI LUT with the FCB file. so that it can support read and write erase commands, etc. The comparison between the flash data and the original APP data when burning and reading is in the Pic 4, we can see, the readout data from the flash is totally the same as the original APP FCB. Pic 4 3,SPT program reserve APP FCB The NXP officially released MCUXPresso Secure Provisional Tool can support the function of retaining the customer's FCB, but the SPT tool currently uses the APP FCB to fill in the flashloader FlexSPI FCB. Therefore, if the customer directly uses the old SDK demo which just contains the read command in the LUT to generate an APP with FCB, then use the SPT tool to burn the flash, and choose to keep the customer FCB in the tool, you will encounter the problem of erasing failure. In this case, analyze the reason, we can know the FCB on the customer APP side needs to fill in the full FCB LUT command, that is, including reading, writing, erasing, etc. The following shows how the old original SDK led_blinky generates an image with an FCB header and writes it in the SPT tool. As you can see in Pic 5, the tool has information that if you use APP FCB, you need to ensure that the FCB LUT contains the read, erase, program commands. Pic 6 shows the programming situation of APP FCB LUT only including read. It has failed when doing erase. The reason is that there is no erase, program and other commands in the FlexSPI LUT command, so it will fail when doing the corresponding erasing or programming.   Pic 5 Pic 6 Pic 7 If you look at the specific command, as shown in Pic 7, you can find that the SPT tool directly uses the FCB header extracted from the APP image to flash the LUT of the flashloader FlexSPI, so there will be no erase and write commands, and it will fail when erasing. The following is how to fill in the LUT in the FCB of the SDK, open evkmimxrt1060_flexspi_nor_config.c, and modify the FCB as follows: const flexspi_nor_config_t qspiflash_config = {     .memConfig =         {             .tag              = FLEXSPI_CFG_BLK_TAG,             .version          = FLEXSPI_CFG_BLK_VERSION,             .readSampleClksrc=kFlexSPIReadSampleClk_LoopbackFromDqsPad,             .csHoldTime       = 3u,             .csSetupTime      = 3u,             .sflashPadType    = kSerialFlash_4Pads,             .serialClkFreq    = kFlexSpiSerialClk_100MHz,             .sflashA1Size     = 8u * 1024u * 1024u,             .lookupTable =                 {                   // Read LUTs                   FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0xEB, RADDR_SDR, FLEXSPI_4PAD, 0x18),                   FLEXSPI_LUT_SEQ(DUMMY_SDR, FLEXSPI_4PAD, 0x06, READ_SDR, FLEXSPI_4PAD, 0x04),                   // Read status                   [4*1] = FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0x05, READ_SDR, FLEXSPI_1PAD, 0x04),                   //write Enable                   [4*3] = FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0x06, STOP, FLEXSPI_1PAD, 0),                   // Sector Erase byte LUTs                   [4*5] = FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0x20, RADDR_SDR, FLEXSPI_1PAD, 0x18),                   // Block Erase 64Kbyte LUTs                   [4*8] = FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0xD8, RADDR_SDR, FLEXSPI_1PAD, 0x18),                    //Page Program - single mode                   [4*9] = FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0x02, RADDR_SDR, FLEXSPI_1PAD, 0x18),                   [4*9+1] = FLEXSPI_LUT_SEQ(WRITE_SDR, FLEXSPI_1PAD, 0x04, STOP, FLEXSPI_1PAD, 0x0),                   //Erase whole chip                   [4*11] =FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0x60, STOP, FLEXSPI_1PAD, 0),                                       },         },     .pageSize           = 256u,     .sectorSize         = 4u * 1024u,     .blockSize          = 64u * 1024u,     .isUniformBlockSize = false, }; Please note, after the internal SDK team modification, from SDK_2_12_0_EVK-MIMXRT1060, the evkmimxrt1060_flexspi_nor_config.c already add LUT cmd to the full FCB LUT function. Use the above FCB to generate the APP, then use the SPT tool to burn the app with customer FCB again, we can see, the programming is working now. Pic 8 In summary, if you need to reserve the customer FCB, you can use the above method, but if you use the SPT tool, you need to add read, write, and erase commands to the LUT of the code FCB to ensure that flexSPI successfully operates the external flash.
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Issue: 802.11 IEEE station Power Save mode is not working as expected with the latest SDK 2.11.1, supporting NXP wireless solutions 88W8987/88W8977/IW416.   Solution: Modify the structure in file : middleware/wifi/wifidriver/incl/mlan_fw.h, Replace  “ENH_PS_MODES action” to “uint16_t action”.    Note: This fix will officially be part of SDK: 2.12.0
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RT106L_S voice control system based on the Baidu cloud 1 Introduction     The NXP RT106L and RT106S are voice recognition chip which is used for offline local voice control, SLN-LOCAL-IOT is based on RT106L, SLN-LOCAL2-IOT is a new local speech recognition board based on RT106S. The board includes the murata 1DX wifi/BLE module, the AFE voice analog front end, the ASR recognition system, the external flash, 2 microphones, and the analog voice amplifier and speakers. The voice recognition process for SLN-LOCAL-IOT and SLN-LOCAL2-IOT is different and the new SLN-LOCAL2-IOT is recommended.     This article is based on the voice control board SLN-LOCAL/2-IOT to implement the following block diagram functions: Pic 1 Use the PC-side speed model tool (Cyberon DSMT) to generate WW(wake word) and VC(voice command) Command related voice engine binary files , which will be used by the demo code. This system is mainly used for the Chinese word recognition, when the user says Chinese word: "小恩小恩", it wakes up SLN-LOCAL/2-IOT, and the board gives feedback "小恩来了,请吩咐". Then system enter the voice recognition stage, the user can say the voice recognition command: “开红灯”,“关红灯”,“开绿灯”,“关绿灯”,“灯闪烁”,“开远程灯”,“关远程灯”, after recognition, the board gives feedback "好的". Among them, “开红灯”,“关红灯”,“开绿灯”,“关绿灯”,“灯闪烁”,the five commands are used for the local light switch, while the 开远程灯”,“关远程灯“two commands can through network communication Baidu cloud control the additional MIMXRT1060-EVK development board light switch. SLN-LOCAL/2-IOT through the WIFI module access to the Internet with MQTT protocol to achieve communication with Baidu cloud, when dectect the remote control command, publish the json packets to Baidu cloud, while MIMRT1060-EVK subscribe Baidu cloud data, will receive data from the IOT board and analyze the EVK board led control. PC side can use MQTT.fx software to subscribe the Baidu cloud data, it also can send data to the device to achieve remote control function directly.  Now, will give the detail content about how to use the SLN-LOCAL/2-IOT SDK demo realize the customized Chinese wake command and voice command, and remote control the MIMXRT1060-EVK through the Baidu Cloud.     2 Platform establish 2.1 Used platform SLN-LOCAL-IOT/SLN-LOCAL2-IOT MIMXRT1060-EVK MQTT.fx SDK_2_8_0_SLN-LOCAL2-IOT MCUXPresso IDE Segger JLINK Baidu Smart Cloud: Baidu cloud control+ TTS Audacity:audio file format convert tool WAVToCode:wav convert to the c array code, which used for the demo tilte play MCUBootUtility: used to burn the feedback audio file to the filesystem Cyberon DSMT: wake word and voice detect command generation tool DSMT is the very important tool to realize the wake word and voice dection, the apply follow is: Pic 2 2.2 Baidu Smart cloud 2.2.1 Baidu cloud IOT control system Enter the IoT Hub: https://cloud.baidu.com/product/iot.html     Click used now. 2.2.1.1 Create device project Create a project, select the device type, and enter the project name. Device types can use shadows as images of devices in the cloud to see directly how data is changing. Once created, an endpoint is generated, along with the corresponding address: Pic 3 2.2.1.2 Create Thing model The Thing model is mainly to establish various properties needed in the shadow, such as temperature, humidity, other variables, and the type of value given, in fact, it is also the json item in the actual MQTT communication.    Click the newly created device-type project where you can create a new thing model or shadow: Pic 4    Here create 3 attributes:LEDstatus,humid,temp It is used to represent the led status, humidity, temperature and so on, which is convenient for communication and control between the cloud and RT board. Once created, you get the following picture:   Pic 5   2.2.1.3 Create Thing shadow In the device-type project, you can select the shadow, build your own shadow platform, enter the name, and select the object model as the newly created Thing model containing three properties, after the create, we can get the details of the shadow:   Pic 6 At the same time will also generate the shadow-related address, names and keys, my test platform situation is as follows: TCP Address: tcp://rndrjc9.mqtt.iot.gz.baidubce.com:1883 SSL Address: ssl://rndrjc9.mqtt.iot.gz.baidubce.com:1884 WSS Address: wss://rndrjc9.mqtt.iot.gz.baidubce.com:443 name: rndrjc9/RT1060BTCDShadow key: y92ewvgjz23nzhgn Port 1883, does not support transmission data encryption Port 1884, supports SSL/TLS encrypted transmission Port 8884, which supports wesockets-style connections, also contains SSL encryption. This article uses a 1883 port with no transmission data encryption for easy testing. So far, Baidu cloud device-type cloud shadow has been completed, the following can use MQTTfx tools to connect and test. In practice, it is recommended that customers build their own Baidu cloud connection, the above user key is for reference only.   2.2.2 Online TTS    SLN-LOCAL/2-IOT board recognizes wake-up words, recognition words, or when powering on, you need to add corresponding demo audio, such as: "百度云端语音测试demo ", "小恩来啦!请吩咐“,"好的". These words need to do a text-to-wav audio file synthesis, here is Baidu Smart Cloud's online TTS function, the specific operation can refer to the following documents: https://ai.baidu.com/ai-doc/SPEECH/jk38y8gno   Once the base audio library is opened, use the main.py provided in the link above and modify it to add the Chinese field you want to convert to the file "TEXT" and add the audio file to be converted in "save_file" such as xxx .wav, using the command: python main.py to complete the conversion, and generate the audio format corresponding to the text, such as .mp3, .wav. Pic 7   After getting the wav file, it can’t be used directly, we need to note that for SLN-LOCAL/2-IOT board, you need to identify the audio source of the 48K sample rate with 16bit, so we need to use the Audioacity Audio tool to convert the audio file format to 48K16bit wav. Import 16K16bit wav files generated by Baidu TTS into the Audioacity tool, select project rate of 48Khz, file->export->export as WAV, select encoding as signed 16bit PCM, and regenerate 48Khz16bit wav for use. Pic 8 “百度云端语音测试demo“:Used for power-on broadcasting, demo name broadcasting, it is stored in RT demo code, so you need to convert it to a 16bit C code array and add it to the project. "小恩来啦!请吩咐",“好的“:voice detect feedback, it is saved in the filesystem ZH01,ZH02 area. 2.3 playback audio data prepare and burn   There are two playback audio file, it is "小恩来啦!请吩咐",“好的“,it is saved in the filesystem ZH01,ZH02 area. Filesystem memory map like this: Pic 9 So, we need to convert the 48K16bit wav file to the filesystem needed format, we need to use the official tool::Ivaldi_sln_local2_iot Reference document:SLN-LOCAL2-IOT-DG chapter 10.1 Generating filesystem-compatible files Use bash input the commands like the following picture: Pic10 Use the convert command to get the playback bin file: python file_format.py -if xiaoencoming_48k16bit.wav -of xiaoencoming_48k16bit.bin -ft H At last, it will generate the file: "小恩来啦!请吩咐"->xiaoencoming_48k16bit.bin,burn to flash address 0x6184_0000 “好的”->OK_48k16bit.bin, burn to flash address 0x6180_0000 Then, use MCUBootUtility tool burn the above two file to the related images. Here, take OK_48k16bit.bin as an example, demo enter the serial download mode(J27-0), power off and power on. Flash chip need to select hyper flash IS26KSXXS, use the boot device memory windows, write button to burn the .bin file to the related address, length is 0X40000 Pic11 Pic12 xiaoencoming_48k16bit.bin can use the same method to download to 0x6184_0000,Length is 0X40000.   2.4 Demo audio prepare and add The prepared baiduclouddemo_48K16bit.wav(“百度云端语音测试demo “) need to convert to the 16bit C array code, and put to the project code, calls by the code, this is used for the demo mode play. The convert need to use the WAVToCode, the operation like this: Pic 13 The generated baiducloulddemo_48K16bit.c,add it to the demo project C files: sln_local_iot_local_demo->audio->demos->smart_home.c。 2.5 WW and VC prepare Wake-up word are generated through the cyberon DSMT tool, which supports a wide range of language, customers can request the tool through Figure 2. The Chinese wake-up words and voice command words in this article are also generated through DSMT. DSMT can have multiple groups, group1 as a wake-up word configuration, CmdMapID s 1. Other groups act as voice command words, such as CMD-IOT in this article, cmdMapID=2. Pic 14   Pic 15 Wake word continuously detects the input audio stream, uses group1, and if successfully wakes up, will do the voice command detection uses group2, or other identifying groups as well as custom groups. The wake-up words using the DSMT tool, the configuration are as follows: Pic 16 The WW can support more words, customer can add the needed one in the group 1. Use the DSMT configure VC like this: Pic 17 Then, save the file, code used file are: _witMapID.bin, CMD_IOT.xml,WW.xml. In the generated files, CYBase.mod is the base model, WW.mod is the WW model, CMD_IOT.mod is the VC model. After Pic 16,17, it finishes the WW and VC command prepare, we can put the DSMT project to the RT106S demo project folder: sln_local2_iot_local_demo\local_voice\oob_demo_zh 3 Code prepare Based on the official SLN-LOCAL2-IOT SDK local_demo, the code in this article modifies the Chinese wake-up words and recognition words (or you can build a new customer custom group directly), add local voice detect the led status operations, Then feedback Chinese audio, demo Chinese audio, Wifi network communication MQTT protocol code, and Baidu cloud shadow connection publish. Source reference code SDK path: SDK_2_8_0_SLN-LOCAL2-IOT\boards\sln_local2_iot\sln_voice_examples\local_demo   SDK_2_8_0_SLN-LOCAL2-IOT\boards\sln_local2_iot\sln_boot_apps SLN-LOCAL2-IOT and SLN-LOCAL-IOT code are nearly the same, the only difference is that the ASR library file is different, for RT106S (SLN-LOCAL2-IOT) using SDK it’s own libsln_asr.a library, for RT106L (SLN-LOCAL-IOT) need to use the corresponding libsln_asr_eval.a library.    Importing code requires three projects: local_demo, bootloader, bootstrap. The three projects store in different spaces. See SLN-LOCAL2-IOT-DG .pdf, chapter 3.3 Device memory map    This is the 3 chip project boot process: Pic 18 This document is for demo testing and requires debug, so this article turns off the encryption mechanism, configures bootloader, bootstrap engineering macro definition: DISABLE_IMAGE_VERIFICATION = 1, and uses JLINK to connect SLN-LOCAL/2-IOT's SWD interface to burn code. The following is to add modification code for app local_demo projects. 3.1 sln-local/2-iot code Sln-local-iot, sln-local2-iot platform, the following modification are the same for the two platform. 3.1.1 Voice recognition related code 1)Demo audio play Play content:“百度云端语音测试demo“ sln_local2_iot_local_demo_xe_ledwifi\audio\demos\ smart_home.c content is replaced by the previously generated baiducloulddemo_48K16bit.C. audio_samples.h,modify: #define SMART_HOME_DEMO_CLIP_SIZE 110733 This code is used for the main.c announce_demo API play:         case ASR_CMD_IOT:             ret = demo_play_clip((uint8_t *)smart_home_demo_clip, sizeof(smart_home_demo_clip));   2)command print information #define NUMBER_OF_IOT_CMDS      7 IndexCommands.h static char *cmd_iot_en[] = {"Red led on", "Red led off", "Green led on", "Green led off",                              "cycle led",        "remote led on",         "remote led off"}; static char *cmd_iot_zh[] = {"开红灯", "关红灯", "开绿灯", "关绿灯", "灯闪烁", "开远程灯", "关远程灯"}; Here is the source code modification using IOT, you can actually add your own speech recognition group directly, and add the relevant command identification.   3)sln_local_voice.c Line757 , add led-related notification information in ASR_CMD_IOT mode. oob_demo_control.ledCmd = g_asrControl.result.keywordID[1];     The code is used to obtain the recognized VC command data, and the value of keywordID[1] represents the number. This number can let the code know which detail voice is detected. so that you can do specific things in the app based on the value of ledcmd. The value of keywordID[1] corresponds to Command List in Figure 17. For example, “开远程灯“, if woke up, and recognized "开远程灯", then keywordID[1] is 5, and will transfer to oob_demo_control.ledCmd, which will be used in the appTask API to realize the detail control. 4) main.c void appTask(void *arg) Under case kCommandGeneric: if the language is Chinese, then add the recognition related control code, at first, it will play the feedback as “好的”. Then, it will check the voice detect value, give the related local led control. else if (oob_demo_control.language == ASR_CHINESE) { // play audio "OK" in Chinese #if defined(SLN_LOCAL2_RD) ret = audio_play_clip((uint8_t *)AUDIO_ZH_01_FILE_ADDR, AUDIO_ZH_01_FILE_SIZE); #elif defined(SLN_LOCAL2_IOT) ret = audio_play_clip(AUDIO_ZH_01_FILE); #endif //kerry add operation code==================================================begin RGB_LED_SetColor(LED_COLOR_OFF); if (oob_demo_control.ledCmd == LED_RED_ON) { RGB_LED_SetColor(LED_COLOR_RED); vTaskDelay(5000); } else if (oob_demo_control.ledCmd == LED_RED_OFF) { RGB_LED_SetColor(LED_COLOR_OFF); vTaskDelay(5000); } else if (oob_demo_control.ledCmd == LED_BLUE_ON) { RGB_LED_SetColor(LED_COLOR_BLUE); vTaskDelay(5000); } else if (oob_demo_control.ledCmd == LED_BLUE_OFF) { RGB_LED_SetColor(LED_COLOR_OFF); vTaskDelay(5000); } else if (oob_demo_control.ledCmd == CYCLE_SLOW) { for (int i = 0; i < 3; i++) { RGB_LED_SetColor(LED_COLOR_RED); vTaskDelay(400); RGB_LED_SetColor(LED_COLOR_OFF); RGB_LED_SetColor(LED_COLOR_GREEN); vTaskDelay(400); RGB_LED_SetColor(LED_COLOR_OFF); RGB_LED_SetColor(LED_COLOR_BLUE); vTaskDelay(400); } } … } In addition to local voice recognition control, this article also add remote control functions, mainly through wifi connection, use the mqtt protocol to connect Baidu cloud server, when local speech recognition get the remote control command, it publish the corresponding control message to Baidu cloud, and then the cloud send the message to the client which subscribe this message,  after the client get the message, it will refer to the message content do the related control.   3.1.3 Network connection code 1)sln_local2_iot_local_demo_xe_ledwifi\lwip\src\apps\mqtt     Add mqtt.c 2)sln_local2_iot_local_demo_xe_ledwifi\lwip\src\include\lwip\apps Add mqtt.h, mqtt_opts.h,mqtt_prv.h The related mqtt driver is from the RT1060 sdk, which already added in the attachment project. 3)sln_tcp_server.c   Add MQTT application layer API function code, client ID, server host, MQTT server port number, user name, password, subscription topic, publishing topic and data, etc., more details, check the attachment code.    The MQTT application code is ported from the mqtt project of the RT1060 SDK and added to the sln_tcp_server.c. TCP_OTA_Server function is used to initialize the wifi network, realize wifi connection, connect to the network, resolve Baidu cloud server URL to get IP, and then connect Baidu cloud server through mqtt, after the successful connection, publish the message at first, so that after power-up through mqttfx to see whether the power on network publishing message is successful. TCP_OTA_Server function code is as follows: static void TCP_OTA_Server(void *param) //kerry consider add mqtt related code { err_t err = ERR_OK; uint8_t status = kCommon_Failed; #if USE_WIFI_CONNECTION /* Start the WiFi and connect to the network */ APP_NETWORK_Init(); while (status != kCommon_Success) { status_t statusConnect; statusConnect = APP_NETWORK_Wifi_Connect(true, true); if (WIFI_CONNECT_SUCCESS == statusConnect) { status = kCommon_Success; } else if (WIFI_CONNECT_NO_CRED == statusConnect) { APP_NETWORK_Uninit(); /* If there are no credential in flash delete the TPC server task */ vTaskDelete(NULL); } else { status = kCommon_Failed; } } #endif #if USE_ETHERNET_CONNECTION APP_NETWORK_Init(true); #endif /* Wait for wifi/eth to connect */ while (0 == get_connect_state()) { /* Give time to the network task to connect */ vTaskDelay(1000); } configPRINTF(("TCP server start\r\n")); configPRINTF(("MQTT connection start\r\n")); mqtt_client = mqtt_client_new(); if (mqtt_client == NULL) { configPRINTF(("mqtt_client_new() failed.\r\n");) while (1) { } } if (ipaddr_aton(EXAMPLE_MQTT_SERVER_HOST, &mqtt_addr) && IP_IS_V4(&mqtt_addr)) { /* Already an IP address */ err = ERR_OK; } else { /* Resolve MQTT broker's host name to an IP address */ configPRINTF(("Resolving \"%s\"...\r\n", EXAMPLE_MQTT_SERVER_HOST)); err = netconn_gethostbyname(EXAMPLE_MQTT_SERVER_HOST, &mqtt_addr); configPRINTF(("Resolving status: %d.\r\n", err)); } if (err == ERR_OK) { configPRINTF(("connect to mqtt\r\n")); /* Start connecting to MQTT broker from tcpip_thread */ err = tcpip_callback(connect_to_mqtt, NULL); configPRINTF(("connect status: %d.\r\n", err)); if (err != ERR_OK) { configPRINTF(("Failed to invoke broker connection on the tcpip_thread: %d.\r\n", err)); } } else { configPRINTF(("Failed to obtain IP address: %d.\r\n", err)); } int i=0; /* Publish some messages */ for (i = 0; i < 5;) { configPRINTF(("connect status enter: %d.\r\n", connected)); if (connected) { err = tcpip_callback(publish_message_start, NULL); if (err != ERR_OK) { configPRINTF(("Failed to invoke publishing of a message on the tcpip_thread: %d.\r\n", err)); } i++; } sys_msleep(1000U); } vTaskDelete(NULL); } Please note the following published json data, it can’t be publish directly in the code. {   "reported": {     "LEDstatus": false,     "humid": 88,     "temp": 22   } } Which need to use this web https://www.bejson.com/ realize the json data compression and convert: {\"reported\" : {     \"LEDstatus\" : true,     \"humid\" : 88,     \"temp\" : 11    } }   4)main appTask Under case kCommandGeneric: , if the language is Chinese, then add the corresponding voice recognition control code. "开远程灯": turn on the local yellow light, publish the “remote led on” mqtt message to Baidu cloud, control remote 1060EVK board lights on. "关远程灯": turn on the local white light, publish the “remote led off” mqtt message to Baidu cloud, control the remote 1060EVK board light off. Related operation code: else if (oob_demo_control.ledCmd == LED_REMOTE_ON) { RGB_LED_SetColor(LED_COLOR_YELLOW); vTaskDelay(5000); err_t err = ERR_OK; err = tcpip_callback(publish_message_on, NULL); if (err != ERR_OK) { configPRINTF(("Failed to invoke publishing of a message on the tcpip_thread: %d.\r\n", err)); } } else if (oob_demo_control.ledCmd == LED_REMOTE_OFF) { RGB_LED_SetColor(LED_COLOR_WHITE); vTaskDelay(5000); err_t err = ERR_OK; err = tcpip_callback(publish_message_off, NULL); if (err != ERR_OK) { configPRINTF(("Failed to invoke publishing of a message on the tcpip_thread: %d.\r\n", err)); } } 3.2 MIMXRT1060-EVK code The main function of the MIMXRT1060-EVK code is to configure another client in the cloud, subscribe to the message published by SLN-LOCAL/2-IOT which detect the remote command, and then the LED on the control board is used to test the voice recognition remote control function, this code is based on Ethernet, through the Ethernet port on the board, to achieve network communication, and then use mqtt to connect baidu cloud, and subscribe the message from local2, This enables the reception and execution of the Local2 command. the network code part is similar to SLN-LOCAL2-IOT board network code, the servers, cloud account passwords, etc. are all the same, the main function is to subscribe messages. See the code from attachment RT1060, lwip_mqtt_freertos.c file. When receives data published by the server, it needs to do a data analysis to get the status of the led light and then control it. Normal data from Baidu cloud shadow sent as follows Received 253 bytes from the topic "$baidu/iot/shadow/RT1060BTCDShadow/update/accepted": "{"requestId":"2fc0ca29-63c0-4200-843f-e279e0f019d3","reported":{"LEDstatus":false,"humid":44,"temp":33},"desired":{},"lastUpdatedTime":{"reported":{"LEDstatus":1635240225296,"humid":1635240225296,"temp":1635240225296},"desired":{}},"profileVersion":159}" Then you need to parse the data of LEDstatus from the received data, whether it is false or true. Because the amount of data is small, there is no json-driven parsing here, just pure data parsing, adding the following parsing code to the mqtt_incoming_data_cb function: mqtt_rec_data.mqttindex = mqtt_rec_data.mqttindex + len; if(mqtt_rec_data.mqttindex >= 250) { PRINTF("kerry test \r\n"); PRINTF("idex= %d", mqtt_rec_data.mqttindex); datap = strstr((char*)mqtt_rec_data.mqttrecdata,"LEDstatus"); if(datap != NULL) { if(!strncmp(datap+11,strtrue,4))//char strtrue[]="true"; { GPIO_PinWrite(GPIO1, 3, 1U); //pull high PRINTF("\r\ntrue"); } else if(!strncmp(datap+11,strfalse,5))//char strfalse[]="false"; { GPIO_PinWrite(GPIO1, 3, 0U); //pull low PRINTF("\r\nfalse"); } } mqtt_rec_data.mqttindex =0; It use the strstr search the “LEDstatus“ in the received data, and get the pointer position, then add the fixed length to get the LED status is true or flash. If it is true, turn on the led, if it is false, turn off the led. 4 Test Result    This section gives the test results and video of the system. Before testing the voice function, first use MQTTfx to test baidu cloud connection, release, subscription is no problem, and then test sln-local2-iot combined with mimxrt1060-evk voice wake-up recognition and remote control functions.    For SLN-LOCAL2-IOT wifi hotspot join, enter the command in the print terminal: setup AWS kerry123456   4.1 MQTT.fx test baidu cloud connection MQTT.fx is an EclipsePaho-based MQTT client tool written in the Java language that supports subscription and publishing of messages through Topic.    4.1.1 MQTT fx configuration     Download and install the tool, then open it, at first, need to do the configuration, click edit connection: Pic19 Profile name:connect name Profile type: MQTT broker Broker address: It is the baidu could generated broker address, with 1883 no encryption transfer. Broker port:1883 No encryption Client ID: RT1060BTCDShadow, here need to note, this name should be the same as the could shadow name, otherwise, on the baidu webpage, the connection is not be detected. If this Client ID name is the same as the shadow name, then when the MQTT fx connect, the online side also can see the connection is OK. User credentials: add the thing User name and password from the baidu cloud. After the configuration, click connect, and refresh the website. Before conection: Pic 20 After connection: Pic 21 4.1.2 MQTT fx subscribe When it comes to subscription publishing, what is the topic of publishing subscriptions?  Here you can open your thing shadow, select the interaction, and see that the page has given the corresponding topic situation: Pic 22 Subscribe topic is: $baidu/iot/shadow/RT1060BTCDShadow/update/accepted  Publish topic is: $baidu/iot/shadow/RT1060BTCDShadow/update Pic 23 Click subscribe, we can see it already can used to receive the data.   4.1.3 MQTT fx publish Publish need to input the topic: $baidu/iot/shadow/RT1060BTCDShadow/update It also need to input the content, it will use the json content data. Pic 24 Here, we can use this json data: {   "reported" : {     "LEDstatus" : true,     "humid" : 88,     "temp" : 11    } } The json data also can use the website to check the data: https://www.bejson.com/jsonviewernew/ Pic 25 Input the publish data, and click pubish button: Pic 26 4.1.4 Publish data test result   Before publish, clean the website thing data: Pic 27 MQTT fx publish data, then check the subscribe data and the website situation: Pic 28 We can see, the published data also can be see in the website and the mqttfx subscribe area. Until now, the connection, data transfer test is OK.   4.2 Voice recognition and remote control test This is the device connection picture: Pic 29 4.2.1 voice recognition local control Pic 30 This is the SLN-LOCAL2-IOT print information after recognize the voice WW and VC. Red led on: led cycle: 4.2.2 voice recognition remote control   Following test, wakeup + remote on, wakeup+remote off, and also give the print result and the video. Pic 31 remote control:  
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Face recognition Actually, face recognition technology is used in many scenes in our daily life, for instance, when taking pictures with the mobile phone, the camera software will automatically recognize the faces in the lens and focus, scan face for real-name verification when registering the App and scan face for pay, etc. The basic steps of face recognition are shown in the below figure. Firstly, the camera captures image data, then through preprocessing such as noise elimination and image format conversion, the image data will be transmitted to the processor for face detection and recognition calculations. After recognizing the face successful, continue to do the follow-up operations. Fig1 The basic steps of face recognition i.MX RT106F MCU based solution for face recognition The below figure is the block diagram of i.MX RT106F MCU-based solution for face recognition provided by the NXP. Comparing with the general processor (CPU) solution, it has comparative advantages in cost and power consumption. Further, the PCB size will be smaller too and the MCU usually can boot up within a few hundred milliseconds even with RTOS, versus to the boot-up speed of the processor (CPU) equipped with a Linux system that is about 10 seconds, it will give customers a better user experience. Fig2 i.MX RT106F MCU based solution for face recognition Of course, the i.MX RT106F MCU-based solution face recognition solution is not intended to replace the solution based on the processor (CPU). As aforementioned, face recognition technology has a lot of application cases, and it will definitely be used in more fields in the future, so the MCU-based face recognition solution provides customers and the market with another choice. i.MX RT106F MCU The i.MX RT106F face recognition crossover processor is an EdgeReady™ solution-specific variant of the i.MX RT1060 family of crossover processors, targeting face recognition applications. It features NXP’s advanced implementation of the Arm Cortex®-M7 core, which operates at speeds up to 600 MHz to provide high CPU performance and the best real-time response. i.MX RT106F based solutions enable system designers to easily and inexpensively add face recognition capabilities to a wide variety of smart appliances, smart homes, smart retail, and smart industrial devices. The i.MX RT106F is licensed to run the OASIS Lite library for face recognition (as the below figure shows) which include: Face detection Anti-spoofing Face tracking Face alignment Glass detection Face recognition Confidence measure Face recognition quantified results, etc Fig3 OASIS Recognition Software Pipeline sln_viznas_iot_elock_oobe The sln_viznas_iot_elock_oobe project is the application on the SLN-VIZNAS-IOT (as the below figure shows, regarding the Bootstrap and Bootloader in the software flowchart, I will introduce them in the future). The following development work is based on the sln_viznas_iot_elock_oobe project, however, I need to sketch the basic workflow of it prior to starting real development work. Fig4 SLN-VIZNAS-IOT software flowchart sln_viznas_iot_elock_oobe's workflow flow In the Camera_Start() function, the task (Camera_Init_Task) completes the initialization of the RGB and IR cameras, then creates a task (Camera_Task); In the Display_Start() function, after the task (Display_Init_Task) completes the initialization of the display medium (USB or LCD), it immediately creates the task (Display_Task) and sends the message queue s_DisplayReqMsg.id = QMSG_DISPLAY_FRAME_REQ to the task (Camera_Task), then the pDispData will point to the s_BufferLcd[0] array for storing the image data to be displayed; In the Oasis_Start() function, firstly, OASISLT_init() completes the initialization of the OAISIT library, then creates a task (Oasis_Task) to send the message queues gFaceDetReqMsg.id = QMSG_FACEREC_FRAME_REQ and gFaceInfoMsg.id = QMSG_FACEREC_INFO_UPDATE to the task (Camera_Task) to make the pDetIR and pDetRGB point to the face block diagram captured by the RGB and IR cameras, and update the content pointed by infoMsgIn. After the camera is initialized, the RGB camera works at first. After the image data is captured, an interrupt is triggered and the callback function Camera_Callback() sends the message queue DQMsg.id = QMSG_CAMERA_DQ to the task (Camera_Task), and DQIndex++; CAMERA_RECEIVER_GetFullBuffer() extracts the image data captured by the RGB camera, and sends the message queue DPxpMsg.id = QMSG_PXP_DISPLAY to the task (PXP_Task) created in the APP_PXP_Start() function and EQIndex++, meanwhile switch the camera from RGB to IR. After the APP_PXPStartCamera2Display() function in the task (PXP_Task) completes processing, it sends the message queue s_DResMsg.id = QMSG_PXP_DISPLAY to the task (Camera_Task), and the task (Camera_Task) sends the message queue DresMsg.id = QMSG_DISPLAY_FRAME_RES to the task (Display_Task) after receiving the above message queue. The task (Display_Task) completes display, then it sends the message queue s_DisplayReqMsg.id = QMSG_DISPLAY_FRAME_REQ to the task (Camera_Task) to make pDispData point to the s_BufferLcd[1] array; After the IR camera completes capturing work, CAMERA_RECEIVER_GetFullBuffer() extracts the image data and sends the message queue DPxpMsg.id = QMSG_PXP_DISPLAY to the (PXP_Task) task created in the APP_PXP_Start() function, continue to execute EQIndex++ and switch to RGB camera again, and repeat the steps 5. Finally, send the message queue FPxpMsg.id = QMSG_PXP_FACEREC to the task (PXP_Task) and set irReady = true. After the task (PXP_Task) receives the above message queue, it calls APP_PXPStartCamera2DetBuf() and after completes the processing, sends the message queue s_FResMsg.id = QMSG_PXP_FACEREC to the task (Camera_Task); CAMERA_RECEIVER_GetFullBuffer() extracts the image data collected by the RGB camera, repeat step 5, when (pDetRGB && irReady) condition is met, send the message queue FPxpMsg.id = QMSG_PXP_FACEREC to the task (PXP_Task) and set irReady = false, pDetRGB = NULL, pDetIR = NULL. After the task (PXP_Task) receives the above message queue, it calls APP_PXPStartCamera2DetBuf() and after completes the processing, sends the message queue s_FResMsg.id = QMSG_PXP_FACEREC to the task (Camera_Task). At this time, the (!pDetIR && !pDetRGB) condition is met and the Queue message FResMsg.id = QMSG_FACEREC_FRAME_RES is sent to the task (Oasis_Task), run OASISLT_run_extend to perform face recognition calculation, and send the message queue gFaceDetReqMsg.id = QMSG_FACEREC_FRAME_REQ to the task (Camera_Task) to make the pDetIR and pDetRGB point to the face block diagram captured by the RGB and IR cameras again. keep repeat steps 6 and 7; Fig5 sln_viznas_iot_elock_oobe's workflow flow Smart Coffee machine Fig 6 is the workflow of the smart coffee machine that I want to develop for, as there is no LCD board on hand, in the below development process, I will select Win10's camera (as the below figure shows) to output the captured image, further, take advantage of the Shell command to simulate the LCD's touch feature to interact with the board.   Fig6 workflow of the smart coffee machine Fig7 Camera Code modification In the commondef.h, add a new member variable 'uint16_t coffee_taste' in Union FeatureItem to stand for the favorite coffee taste; typedef union { struct { /*put char/unsigned char together to avoid padding*/ unsigned char magic; char name[FEATUREDATA_NAME_MAX_LEN]; int index; // this id identify a feature uniquely,we should use it as a handler for feature add/del/update/rename uint16_t id; uint16_t pad; // Add a new component uint16_t coffee_taste; /*put feature in the last so, we can take it as dynamic, size limitation: * (FEATUREDATA_FLASH_PAGE_SIZE * 2 - 1 - FEATUREDATA_NAME_MAX_LEN - 4 - 4 -2)/4*/ float feature[0]; }; unsigned char raw[FEATUREDATA_FLASH_PAGE_SIZE * 2]; } FeatureItem; // 1kB   In featuredb.h, add two member functions into class FeatureDB:  set_taste()  and  get_taste() , and add the definition of the above two member functions in featuredb.cpp; class FeatureDB { public: FeatureDB(); ~FeatureDB(); int add_feature(uint16_t id, const std::string name, float *feature); int update_feature(uint16_t id, const std::string name, float *feature); int del_feature(uint16_t id, std::string name); int del_feature(const std::string name); int del_feature_all(); std::vector<std::string> get_names(); int get_name(uint16_t id, std::string &name); std::vector<uint16_t> get_ids(); int ren_name(const std::string oldname, const std::string newname); int feature_count(); int get_free(int &index); int database_save(int count); int get_feature(uint16_t id, float *feature); void set_autosave(bool auto_save); bool get_autosave(); //Add two customize member functions int set_taste(const std::string username, uint16_t taste_number); int get_taste(const std::string username); private: bool auto_save; int load_feature(); int erase_feature(int index); int save_feature(int index = 0); int reassign_feature(); int get_free_mapmagic(); int get_remain_map(); }; int FeatureDB::set_taste(const std::string username, uint16_t taste_number) { int index = FEATUREDATA_MAX_COUNT; for (int i = 0; i < FEATUREDATA_MAX_COUNT; i++) { if (s_FeatureData.item[i].magic == FEATUREDATA_MAGIC_VALID) { if (!strcmp(username.c_str(), s_FeatureData.item[i].name)) { index = i; } } } if (index != FEATUREDATA_MAX_COUNT) { s_FeatureData.item[index].coffee_taste = taste_number; return 0; } else { return -1; } } int FeatureDB::get_taste(const std::string username) { int index = FEATUREDATA_MAX_COUNT; int taste_number; for (int i = 0; i < FEATUREDATA_MAX_COUNT; i++) { if (s_FeatureData.item[i].magic == FEATUREDATA_MAGIC_VALID) { if (!strcmp(username.c_str(), s_FeatureData.item[i].name)) { index = i; } } } if (index != FEATUREDATA_MAX_COUNT) { taste_number = s_FeatureData.item[index].coffee_taste; return taste_number; } else { return -1; } }   In database.h, add the declarations of  DB_Set_Taste()  and  DB_Get_Taste()  functions, and in database.cpp, add the related codes of the above two functions. These two functions are equivalent to encapsulating the newly added member functions set_taste() and get_taste() of the FeatureDB class; int DB_Del(uint16_t id, std::string name); int DB_Del(string name); int DB_DelAll(); int DB_Ren(const std::string oldname, const std::string newname); int DB_GetFree(int &index); int DB_GetNames(std::vector<std::string> *names); int DB_Count(int *count); int DB_Save(int count); int DB_GetFeature(uint16_t id, float *feature); int DB_Add(uint16_t id, float *feature); int DB_Add(uint16_t id, std::string name, float *feature); int DB_Update(uint16_t id, float *feature); int DB_GetIDs(std::vector<uint16_t> &ids); int DB_GetName(uint16_t id, std::string &names); int DB_GenID(uint16_t *id); int DB_SetAutoSave(bool auto_save); // Add two customize functions int DB_Set_Taste(const std::string username, const uint16_t taste); int DB_Get_Taste(const std::string username); int DB_Set_Taste(const std::string username, const uint16_t taste) { int ret = DB_MGMT_FAILED; ret = DB_Lock(); if (DB_MGMT_OK == ret) { ret = s_DB->set_taste(username, taste); DB_UnLock(); } return ret; } int DB_Get_Taste(const std::string username) { int ret = DB_MGMT_FAILED; ret = DB_Lock(); if (DB_MGMT_OK == ret) { ret = s_DB->get_taste(username); DB_UnLock(); } return ret; } In sln_api.h, add the declarations of the functions  VIZN_SetTaste() ,  VIZN_GetTaste()  and  VIZN_Is_Rec_User() , and add the codes of the above three functions in sln_api.cpp. The VIZN_SetTaste() and VIZN_GetTaste() functions are equivalent to the encapsulation of the DB_Set_Taste() and DB_Get_Taste() functions. Why is it so complicated? To follow the code layering mechanism of the elock_oobe project and reduce the difficulty of code implementation through code layered encapsulation. /** * @brief Set user's favorite coffee taste. * * @Param clientHandle The client handler which required this action * @Param userName Pointer to a buffer which contains the name of the new user. * @Param taste Coffee taste */ vizn_api_status_t VIZN_SetTaste(VIZN_api_client_t *clientHandle, char *UserName, cfg_Coffee_taste taste); /** * @brief Set user's favorite coffee taste. * * @Param clientHandle The client handler which required this action * @Param userName Pointer to a buffer which contains the name of the new user. * @Param taste Pointer to the Coffee taste */ vizn_api_status_t VIZN_GetTaste(VIZN_api_client_t *clientHandle, char *UserName, int *taste); vizn_api_status_t VIZN_Is_Rec_User(VIZN_api_client_t *clientHandle, char *UserName); ~~~~~~~~~ vizn_api_status_t VIZN_SetTaste(VIZN_api_client_t *clientHandle, char *UserName, cfg_Coffee_taste taste) { int32_t status; if (!IsValidUserName(UserName)) { return kStatus_API_Layer_RenameUser_InvalidUserName; } status = DB_Set_Taste(std::string(UserName), (uint16_t)taste); if (status == 0) { return kStatus_API_Layer_Success; } else if (status == -1) { return kStatus_API_Layer_SetTaste_Failed; } } vizn_api_status_t VIZN_GetTaste(VIZN_api_client_t *clientHandle, char *UserName, int *taste) { int32_t status; if (!IsValidUserName(UserName)) { return kStatus_API_Layer_RenameUser_InvalidUserName; } *taste = DB_Get_Taste(std::string(UserName)); if (*taste != -1) { return kStatus_API_Layer_Success; } else { return kStatus_API_Layer_GetTaste_Failed; } } vizn_api_status_t VIZN_Is_Rec_User(VIZN_api_client_t *clientHandle, char *UserName) { if (!IsValidUserName(UserName)) { return kStatus_API_Layer_RenameUser_InvalidUserName; } return kStatus_API_Layer_Success; } In sln_api_init.cpp, declare the variable:  std::string Current_User = "" ; which is used to store the name corresponding to the face after recognition, and add the processing function  Coffee_Rec()  after successful face recognition in the structure variable ops2; std::string Current_User = " "; //Add customize function int Coffee_Rec(VIZN_api_client_t *pClient, face_info_t face_info); client_operations_t ops2 = { .detect = NULL, .recognize = Coffee_Rec,//NULL, .enrolment = NULL, }; //Add customize function int Coffee_Rec(VIZN_api_client_t *pClient, face_info_t face_info) { Current_User = face_info.name; return 1; } In sln_timers.h, increase MS_SYSTEM_LOCKED to extend the locked status time to 25 seconds; ~~~~~~~~ #define MS_SYSTEM_LOCKED 25000 //2000 // MS in which the board is in a locked state after a reg/rec. ~~~~~~~~ In sln_cli.cpp, add three Shell commands: order, set_taste, get_taste to stand for the operations of brewing coffee, setting coffee taste, and checking coffee taste; SHELL_COMMAND_DEFINE(set_taste, (char *)"\r\n\"set_taste username <0|1|2|3|~>\": set user's favorite taste\r\n" "0 - Cappuccino\r\n" "1 - Black Coffee\r\n" "2 - Coffee latte\r\n" "3 - Flat White\r\n" "4 - Cortado\r\n" "5 - Mocha\r\n" "6 - Con Panna\r\n" "7 - Lungo\r\n" "8 - Ristretto\r\n" "9 - Others \r\n", FFI_CLI_SetTasteCommand, SHELL_IGNORE_PARAMETER_COUNT); SHELL_COMMAND_DEFINE(get_taste, (char *)"\r\n\"get_taste username\": return user's favorite taste \r\n", FFI_CLI_GetTasteCommand, SHELL_IGNORE_PARAMETER_COUNT); SHELL_COMMAND_DEFINE(order, (char *)"\r\n\"order <0|1|2|3|~>\": order a favorite taste \r\n", FFI_CLI_OrderCommand, SHELL_IGNORE_PARAMETER_COUNT); ~~~~~~ static shell_status_t FFI_CLI_SetTasteCommand(shell_handle_t shellContextHandle, int32_t argc, char **argv) { if (argc != 3) { SHELL_Printf(shellContextHandle, "Wrong parameters\r\n"); return kStatus_SHELL_Error; } return UsbShell_QueueSendFromISR(shellContextHandle, argc, argv, SHELL_EV_FFI_CLI_SET_TASTE); } static shell_status_t FFI_CLI_GetTasteCommand(shell_handle_t shellContextHandle, int32_t argc, char **argv) { if (argc != 2) { SHELL_Printf(shellContextHandle, "Wrong parameters\r\n"); return kStatus_SHELL_Error; } return UsbShell_QueueSendFromISR(shellContextHandle, argc, argv, SHELL_EV_FFI_CLI_GET_TASTE); } shell_status_t FFI_CLI_OrderCommand(shell_handle_t shellContextHandle, int32_t argc, char **argv) { if (argc > 2) { SHELL_Printf(shellContextHandle, "Wrong parameters\r\n"); return kStatus_SHELL_Error; } return UsbShell_QueueSendFromISR(shellContextHandle, argc, argv, SHELL_EV_FFI_CLI_ORDER); } ~~~~~~ shell_status_t RegisterFFICmds(shell_handle_t shellContextHandle) { SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(list)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(add)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(del)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(rename)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(verbose)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(camera)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(version)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(save)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(updateotw)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(reset)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(emotion)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(liveness)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(detection)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(display)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(wifi)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(app_type)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(low_power)); // Add three Shell commands SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(order)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(set_taste)); SHELL_RegisterCommand(shellContextHandle, SHELL_COMMAND(get_taste)); return kStatus_SHELL_Success; } In sln_cli.cpp, it needs to add corresponding codes for handle order, set_taste, get_taste instructions in task UsbShell_CmdProcess_Task else if (queueMsg.shellCommand == SHELL_EV_FFI_CLI_SET_TASTE) { int coffee_taste = atoi(queueMsg.argv[2]); if (coffee_taste >= Cappuccino && coffee_taste <= Others) { status = VIZN_SetTaste(&VIZN_API_CLIENT(Shell),(char *)queueMsg.argv[1], (cfg_Coffee_taste)coffee_taste); if (status == kStatus_API_Layer_Success) { SHELL_Printf(shellContextHandle, "User: %s like coffee taste: %s \r\n", queueMsg.argv[1], Coffee_type[coffee_taste]); } else { SHELL_Printf(shellContextHandle, "Cannot set coffee taste\r\n"); } } else { SHELL_Printf(shellContextHandle, "Unsupported coffee taste\r\n"); } } else if (queueMsg.shellCommand == SHELL_EV_FFI_CLI_GET_TASTE) { int get_taste_num = 0; status = VIZN_GetTaste(&VIZN_API_CLIENT(Shell),(char *)queueMsg.argv[1], &get_taste_num); if (status == kStatus_API_Layer_Success) { SHELL_Printf(shellContextHandle, "User: %s like coffee taste: %s \r\n", queueMsg.argv[1], Coffee_type[(cfg_Coffee_taste)(get_taste_num)]); } else { SHELL_Printf(shellContextHandle, "Cannot get coffee taste\r\n"); } } else if (queueMsg.shellCommand == SHELL_EV_FFI_CLI_ORDER) { status = VIZN_Is_Rec_User(&VIZN_API_CLIENT(Shell),(char *)Current_User.c_str()); if (status == kStatus_API_Layer_Success) { if (queueMsg.argc == 1) { int get_taste_num = 0; status = VIZN_GetTaste(&VIZN_API_CLIENT(Shell),(char*)Current_User.c_str(), &get_taste_num); if (status == kStatus_API_Layer_Success) { SHELL_Printf(shellContextHandle, "User: %s order the a cup of %s \r\n", Current_User.c_str(), Coffee_type[(cfg_Coffee_taste)(get_taste_num)]); } else { SHELL_Printf(shellContextHandle, "Sorry, please order again, Current user is %s\r\n",Current_User.c_str()); } } else if(queueMsg.argc == 2) { int coffee_taste = atoi(queueMsg.argv[1]); if (coffee_taste >= Cappuccino && coffee_taste <= Others) { status = VIZN_SetTaste(&VIZN_API_CLIENT(Shell),(char*)Current_User.c_str(), (cfg_Coffee_taste)coffee_taste); if (status == kStatus_API_Layer_Success) { SHELL_Printf(shellContextHandle, "User: %s order a cup of %s \r\n", Current_User.c_str(), Coffee_type[coffee_taste]); } else { SHELL_Printf(shellContextHandle, "Cannot set coffee taste, Current user is %s\r\n",Current_User.c_str()); } } else { SHELL_Printf(shellContextHandle, "Unsupported coffee taste\r\n"); } } } } Use the cafe logo of《Friends》to replace the original Welcome_home picture, use the BmpCvt tool to convert the picture into the corresponding array, and add it to welcomehome_320x122.h. static const unsigned short Coffee_shop_320_122[] = { 0x59E6, 0x6227, 0x6247, 0x59C5, 0x59C5, 0x59A5, 0x4103, 0x6A67, 0x6A47, 0x6227, 0x6A47, 0x6A68, 0x7268, 0x6A67, 0x6A67, 0x6A47, 0x72A9, 0x6A68, 0x7268, 0x6A48, 0x5A06, 0x6A88, 0x6A68, 0x6247, 0x6A47, 0x7289, 0x7289, 0x6A47, 0x6A47, 0x6A47, 0x6227, 0x6A68, 0x6206, 0x6A47, 0x5A26, 0x6247, 0x6227, 0x6A27, 0x4924, 0x836D, 0x5207, 0x7BAC, 0x5247, 0x83ED, 0x4A47, 0x2923, 0x7B8C, 0x49E5, 0x49E5, 0x4A05, 0x28C1, 0x5226, 0x6267, 0x6A87, 0x72E9, 0x6267, 0x6AA9, 0x5A27, 0x6AA9, 0x6AA9, 0x5A47, 0x6A88, 0x5A06, 0x5A47, 0x6AA9, 0x5A47, 0x62A9, 0x5206, 0x6288, 0x6268, 0x5A47, 0x5A27, 0x5A47, 0x5A27, 0x49E6, 0x4A07, 0x4A07, 0x5A89, 0x49C6, 0x5A48, 0x5A28, 0x5A47, 0x5226, 0x49E6, 0x49C6, 0x41A6, 0x5208, 0x2082, 0x52A8, 0x6B6B, 0x39A5, 0x39A5, 0x3964, 0x49E7, 0x3104, 0x49C7, 0x3945, 0x41A6, 0x28A2, 0x2061, 0x3965, 0x28E3, 0x1881, 0x3944, 0x3103, 0x3103, 0x3903, 0x4145, 0x51A6, 0x51C6, 0x4985, 0x51E6, 0x51E6, 0x61E7, 0x6A48, 0x6A28, 0x6A28, 0x6A27, 0x61E6, 0x6207, 0x6A68, 0x59E7, 0x4185, 0x51E6, 0x51A6, 0x6228, 0x5A07, 0x6228, 0x5A08, 0x4184, 0x41A5, 0x4164, 0x3944, 0x3944, 0x736B, 0x83ED, 0x41A5, 0x83ED, 0x6288, 0x8BAB, 0x836A, 0x6287, 0x6B2A, 0x5267, 0x83CD, 0x5A68, 0x5228, 0x3986, 0x3985, 0x7B0A, 0x6A67, 0x7267, 0x832B, 0x49A5, 0x6206, 0x8AC9, 0x72A8, 0x82C9, 0x82E9, 0x8309, 0x6A46, 0x8B2B, 0x3860, 0x8329, 0x6A67, 0x7288, 0x7268, 0x61E6, 0x7267, 0x6A67, 0x59C5, 0x51A4, 0x6A46, 0x7AA8, 0x6A26, 0x7287, 0x7AA8, 0x72A8, 0x72A9, 0x51C5, 0x5A27, 0x5A27, 0x3923, 0x ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ 0x7B8C, 0x734B, 0x6B0A, 0x83CD, 0x83ED, 0x8C0E, 0x7B8C, 0x7B6C, 0x20C2, 0x5227, 0x83ED, 0x6AE9, 0x734B, 0x62A9, 0x7B6B, 0x7B8C, 0x62E9, 0x7BAC, 0x7B6B, 0x732A, 0x940D, 0x83AC, 0x732A, 0x7309, 0x8BCC, 0x7309, 0x8BCD, 0x83AC, 0x7B6B, 0x940D, 0x3943, 0x942E, 0x7B6B, 0x734A, 0x7B8B, 0x62C8, 0x7B8B, 0x7B6A, 0x7BAB, 0x732A, 0x7B6B, 0x7B6B, 0x83CC, 0x6B09, 0x6AA9, 0x6AE9, 0x7B6B, 0x7B8B, 0x83AC, 0x734B, 0x6AC9, 0x6B0A, 0x734B, 0x734A, 0x62A8, 0x732A, 0x8C0E, 0x8BCD, 0x944F, 0x734B, 0x7B8B, 0x732A, 0x942E, 0x8BCD, 0x83AD, 0x732B, 0x6B0A, 0x6AEA, 0x62C9, 0x9C90, 0x28C2, 0x8BEE, 0x93EE, 0x8BCD, 0x4183, 0x838B, 0x7B6A, 0x6287, 0x8BCB }; Programming the new project After saving the modified code and recompile the sln_viznas_iot_elock_oobe project (as shown in the figure below), then connect the MCU-LINK to J6 on the SLN-VIZNAS-IOT, just like Fig9 shows. Fig8 Recompile code Fig9 MCU-LINK (Note: it needs to reselect the Flash driver, as the below figure shows.) Fig10 Flash driver After that, it's able to program the code project to the on-board Hyperflash. Test & Summary When the new code project boot-up, please refer to Get Started with the SLN-VIZNAS-IOT to use the serial terminal to test the newly added three Shell commands: orders, set_taste, and get_taste. Once a face is successfully recognized, the cafe logo will appear up (as shown in Fig11). Fig11 Cafe logo Definitely, this smart coffee machine seems like a 'toy' demo, and there is a lot of work to improve it. Below is the list of my future work plans, Use the LCD panel instead of USB to display; Connect an external amplifier to enable voice prompt feature; Enable the Wifi feature to connect to the App; Use the GUI library to enhance UI experience; Add a voice recognition feature to control; And I'll be glad to hear any comments from you.    
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As we know, the RT series MCUs support the XIP (Execute in place) mode and benefit from saving the number of pins, serial NOR Flash is most commonly used, as the FlexSPI module can high efficient fetch the code and data from the Serial NOR flash for Cortex-M7 to execute. The fetch way is implementing via utilizing the Quad IO Fast Read command, meanwhile, the serail NOR flash works in the SDR (Single Data transfer Rate) mode, it receives data on SCLK rise edge and transmits data on SCLK fall edge. Comparing to the SDR mode, the DDR (Dual Data transfer Rate) mode has a higher throughput capacity, whether it can provide better performance of XIP mode, and how to do that if we want the Serial NOR Flash to work in DDR (Dual Data transfer Rate) mode? SDR & DDR mode SDR mode: In SDR (Single Data transfer Rate) mode, data is only clocked on one edge of the clock (either the rising or falling edge). This means that for SDR to have data being transmitted at X Mbps, the clock bit rate needs to be 2X Mbps. DDR mode: For DDR (Dual Data transfer Rate) mode, also known as DTR (Dual Transfer Rate) mode, data is transferred on both the rising and falling edge of the clock. This means data is transmitted at X Mbps only requires the clock bit rate to be X Mbps, hence doubling the bandwidth (as Fig 1 shows).   Fig 1 Enable DDR mode The below steps illustrate how to make the i.MX RT1060 boot from the QSPI with working in DDR mode. Note: The board is MIMXRT1060, IDE is MCUXpresso IDE Open a hello_world as the template Modify the FDCB(Flash Device Configuration Block) a)Set the controllerMiscOption parameter to supports DDR read command. b) Set Serial Flash frequency to 60 MHz. c)Parase the DDR read command into command sequence. The following table shows a template command sequence of DDR Quad IO FAST READ instruction and it's almost matching with the FRQDTR (Fast Read Quad IO DTR) Sequence of IS25WP064 (as Fig 2 shows).   Fig2 FRQDTR Sequence d)Adjust the dummy cycles. The dummy cycles should match with the specific serial clock frequency and the default dummy cycles of the FRQDTR sequence command is 6 (as the below table shows).   However, when the serial clock frequency is 60MHz, the dummy cycle should change to 4 (as the below table shows).   So it needs to configure [P6:P3] bits of the Read Register (as the below table shows) via adding the SET READ PARAMETERS command sequence(as Fig 3 shows) in FDCB manually. Fig 3 SET READ PARAMETERS command sequence In further, in DDR mode, the SCLK cycle is double the serial root clock cycle. The operand value should be set as 2N, 2N-1 or 2*N+1 depending on how the dummy cycles defined in the device datasheet. In the end, we can get an adjusted FCDB like below. // Set Dummy Cycles #define FLASH_DUMMY_CYCLES 8 // Set Read register command sequence's Index in LUT table #define CMD_LUT_SEQ_IDX_SET_READ_PARAM 7 // Read,Read Status,Write Enable command sequences' Index in LUT table #define CMD_LUT_SEQ_IDX_READ 0 #define CMD_LUT_SEQ_IDX_READSTATUS 1 #define CMD_LUT_SEQ_IDX_WRITEENABLE 3 const flexspi_nor_config_t qspiflash_config = { .memConfig = { .tag = FLEXSPI_CFG_BLK_TAG, .version = FLEXSPI_CFG_BLK_VERSION, .readSampleClksrc=kFlexSPIReadSampleClk_LoopbackFromDqsPad, .csHoldTime = 3u, .csSetupTime = 3u, // Enable DDR mode .controllerMiscOption = kFlexSpiMiscOffset_DdrModeEnable | kFlexSpiMiscOffset_SafeConfigFreqEnable, .sflashPadType = kSerialFlash_4Pads, //.serialClkFreq = kFlexSpiSerialClk_100MHz, .serialClkFreq = kFlexSpiSerialClk_60MHz, .sflashA1Size = 8u * 1024u * 1024u, // Enable Flash register configuration .configCmdEnable = 1u, .configModeType[0] = kDeviceConfigCmdType_Generic, .configCmdSeqs[0] = { .seqNum = 1, .seqId = CMD_LUT_SEQ_IDX_SET_READ_PARAM, .reserved = 0, }, .lookupTable = { // Read LUTs [4*CMD_LUT_SEQ_IDX_READ] = FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0xED, RADDR_DDR, FLEXSPI_4PAD, 0x18), // The MODE8_DDR subsequence costs 2 cycles that is part of the whole dummy cycles [4*CMD_LUT_SEQ_IDX_READ + 1] = FLEXSPI_LUT_SEQ(MODE8_DDR, FLEXSPI_4PAD, 0x00, DUMMY_DDR, FLEXSPI_4PAD, FLASH_DUMMY_CYCLES-2), [4*CMD_LUT_SEQ_IDX_READ + 2] = FLEXSPI_LUT_SEQ(READ_DDR, FLEXSPI_4PAD, 0x04, STOP, FLEXSPI_1PAD, 0x00), // READ STATUS REGISTER [4*CMD_LUT_SEQ_IDX_READSTATUS] = FLEXSPI_LUT_SEQ(CMD_SDR, FLEXSPI_1PAD, 0x05, READ_SDR, FLEXSPI_1PAD, 0x01), [4*CMD_LUT_SEQ_IDX_READSTATUS + 1] = FLEXSPI_LUT_SEQ(STOP, FLEXSPI_1PAD, 0x00, 0, 0, 0), // WRTIE ENABLE [4*CMD_LUT_SEQ_IDX_WRITEENABLE] = FLEXSPI_LUT_SEQ(CMD_SDR,FLEXSPI_1PAD, 0x06, STOP, FLEXSPI_1PAD, 0x00), // Set Read register [4*CMD_LUT_SEQ_IDX_SET_READ_PARAM] = FLEXSPI_LUT_SEQ(CMD_SDR,FLEXSPI_1PAD, 0x63, WRITE_SDR, FLEXSPI_1PAD, 0x01), [4*CMD_LUT_SEQ_IDX_SET_READ_PARAM + 1] = FLEXSPI_LUT_SEQ(STOP,FLEXSPI_1PAD, 0x00, 0, 0, 0), }, }, .pageSize = 256u, .sectorSize = 4u * 1024u, .blockSize = 64u * 1024u, .isUniformBlockSize = false, }; Is DDR mode real better? According to the RT1060's datasheet, the below table illustrates the maximum frequency of FlexSPI operation, as the MIMXRT1060's onboard QSPI flash is IS25WP064AJBLE, it doesn't contain the MQS pin, it means set MCR0.RXCLKsrc=1 (Internal dummy read strobe and loopbacked from DQS) is the most optimized option. operation mode RXCLKsrc=0 RXCLKsrc=1 RXCLKsrc=3 SDR 60 MHz 133 MHz 166 MHz DDR 30 MHz 66 MHz 166 MHz In another word, QSPI can run up to 133 MHz in SDR mode versus 66 MHz in DDR mode. From the perspective of throughput capacity, they're almost the same. It seems like DDR mode is not a better option for IS25WP064AJBLE and the following experiment will validate the assumption. Experiment mbedtls_benchmark I use the mbedtls_benchmark as the first testing demo and I run the demo under the below conditions: 100MH, SDR mode; 133MHz, SDR mode; 66MHz, DDR mode; According to the corresponding printout information (as below shows), I make a table for comparison and I mark the worst performance of implementation items among the above three conditions, just as Fig 4 shows. SDR Mode run at 100 MHz. FlexSPI clock source is 3, FlexSPI Div is 6, PllPfd2Clk is 720000000 mbedTLS version 2.16.6 fsys=600000000 Using following implementations: SHA: DCP HW accelerated AES: DCP HW accelerated AES GCM: Software implementation DES: Software implementation Asymmetric cryptography: Software implementation MD5 : 18139.63 KB/s, 27.10 cycles/byte SHA-1 : 44495.64 KB/s, 12.52 cycles/byte SHA-256 : 47766.54 KB/s, 11.61 cycles/byte SHA-512 : 2190.11 KB/s, 267.88 cycles/byte 3DES : 1263.01 KB/s, 462.49 cycles/byte DES : 2962.18 KB/s, 196.33 cycles/byte AES-CBC-128 : 52883.94 KB/s, 10.45 cycles/byte AES-GCM-128 : 1755.38 KB/s, 329.33 cycles/byte AES-CCM-128 : 2081.99 KB/s, 279.72 cycles/byte CTR_DRBG (NOPR) : 5897.16 KB/s, 98.15 cycles/byte CTR_DRBG (PR) : 4489.58 KB/s, 129.72 cycles/byte HMAC_DRBG SHA-1 (NOPR) : 1297.53 KB/s, 448.03 cycles/byte HMAC_DRBG SHA-1 (PR) : 1205.51 KB/s, 486.04 cycles/byte HMAC_DRBG SHA-256 (NOPR) : 1786.18 KB/s, 327.70 cycles/byte HMAC_DRBG SHA-256 (PR) : 1779.52 KB/s, 328.93 cycles/byte RSA-1024 : 202.33 public/s RSA-1024 : 7.00 private/s DHE-2048 : 0.40 handshake/s DH-2048 : 0.40 handshake/s ECDSA-secp256r1 : 9.00 sign/s ECDSA-secp256r1 : 4.67 verify/s ECDHE-secp256r1 : 5.00 handshake/s ECDH-secp256r1 : 9.33 handshake/s   DDR Mode run at 66 MHz. FlexSPI clock source is 2, FlexSPI Div is 5, PllPfd2Clk is 396000000 mbedTLS version 2.16.6 fsys=600000000 Using following implementations: SHA: DCP HW accelerated AES: DCP HW accelerated AES GCM: Software implementation DES: Software implementation Asymmetric cryptography: Software implementation MD5 : 16047.13 KB/s, 27.12 cycles/byte SHA-1 : 44504.08 KB/s, 12.54 cycles/byte SHA-256 : 47742.88 KB/s, 11.62 cycles/byte SHA-512 : 2187.57 KB/s, 267.18 cycles/byte 3DES : 1262.66 KB/s, 462.59 cycles/byte DES : 2786.81 KB/s, 196.44 cycles/byte AES-CBC-128 : 52807.92 KB/s, 10.47 cycles/byte AES-GCM-128 : 1311.15 KB/s, 446.53 cycles/byte AES-CCM-128 : 2088.84 KB/s, 281.08 cycles/byte CTR_DRBG (NOPR) : 5966.92 KB/s, 97.55 cycles/byte CTR_DRBG (PR) : 4413.15 KB/s, 130.42 cycles/byte HMAC_DRBG SHA-1 (NOPR) : 1291.64 KB/s, 449.47 cycles/byte HMAC_DRBG SHA-1 (PR) : 1202.41 KB/s, 487.05 cycles/byte HMAC_DRBG SHA-256 (NOPR) : 1748.38 KB/s, 328.16 cycles/byte HMAC_DRBG SHA-256 (PR) : 1691.74 KB/s, 329.78 cycles/byte RSA-1024 : 201.67 public/s RSA-1024 : 7.00 private/s DHE-2048 : 0.40 handshake/s DH-2048 : 0.40 handshake/s ECDSA-secp256r1 : 8.67 sign/s ECDSA-secp256r1 : 4.67 verify/s ECDHE-secp256r1 : 4.67 handshake/s ECDH-secp256r1 : 9.00 handshake/s   Fig 4 Performance comparison We can find that most of the implementation items are achieve the worst performance when QSPI works in DDR mode with 66 MHz. Coremark demo The second demo is running the Coremark demo under the above three conditions and the result is illustrated below. SDR Mode run at 100 MHz. FlexSPI clock source is 3, FlexSPI Div is 6, PLL3 PFD0 is 720000000 2K performance run parameters for coremark. CoreMark Size : 666 Total ticks : 391889200 Total time (secs): 16.328717 Iterations/Sec : 2449.671999 Iterations : 40000 Compiler version : MCUXpresso IDE v11.3.1 Compiler flags : Optimization most (-O3) Memory location : STACK seedcrc : 0xe9f5 [0]crclist : 0xe714 [0]crcmatrix : 0x1fd7 [0]crcstate : 0x8e3a [0]crcfinal : 0x25b5 Correct operation validated. See readme.txt for run and reporting rules. CoreMark 1.0 : 2449.671999 / MCUXpresso IDE v11.3.1 Optimization most (-O3) / STACK   SDR Mode run at 133 MHz. FlexSPI clock source is 3, FlexSPI Div is 4, PLL3 PFD0 is 664615368 2K performance run parameters for coremark. CoreMark Size : 666 Total ticks : 391888682 Total time (secs): 16.328695 Iterations/Sec : 2449.675237 Iterations : 40000 Compiler version : MCUXpresso IDE v11.3.1 Compiler flags : Optimization most (-O3) Memory location : STACK seedcrc : 0xe9f5 [0]crclist : 0xe714 [0]crcmatrix : 0x1fd7 [0]crcstate : 0x8e3a [0]crcfinal : 0x25b5 Correct operation validated. See readme.txt for run and reporting rules. CoreMark 1.0 : 2449.675237 / MCUXpresso IDE v11.3.1 Optimization most (-O3) / STACK   DDR Mode run at 66 MHz. FlexSPI clock source is 2, FlexSPI Div is 5, PLL3 PFD0 is 396000000 2K performance run parameters for coremark. CoreMark Size : 666 Total ticks : 391890772 Total time (secs): 16.328782 Iterations/Sec : 2449.662173 Iterations : 40000 Compiler version : MCUXpresso IDE v11.3.1 Compiler flags : Optimization most (-O3) Memory location : STACK seedcrc : 0xe9f5 [0]crclist : 0xe714 [0]crcmatrix : 0x1fd7 [0]crcstate : 0x8e3a [0]crcfinal : 0x25b5 Correct operation validated. See readme.txt for run and reporting rules. CoreMark 1.0 : 2449.662173 / MCUXpresso IDE v11.3.1 Optimization most (-O3) / STACK   After comparing the CoreMark scores, it gets the lowest CoreMark score when QSPI works in DDR mode with 66 MHz. However, they're actually pretty close. Through the above two testings, we can get the DDR mode maybe not a better option, at least for the i.MX RT10xx series MCU.
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Created by:  jeremyzhou Introduction Normal Cortex-M core-based MCUs generally have built-in parallel NOR Flash. The parallel NOR Flash is directly hung on the Cortex-M core high-performance AHB bus. If a well-known IDE supports the MCU, it should integrate the corresponding Flash driver algorithm which enables the developer to program and debug the MCU in the IDE. However, the i.MX RT series MCU doesn't contain the internal flash, how do developers debug these MCUs with online XIP (eXecute-In-Place)? Take easy, i.MXRT can support external parallel NOR and serial NOR to run the XIP, benefit from saving the number of pins, serial NOR Flash is most commonly used and FlexSPI supports XIP feature which makes online debug available. The article introduces the mechanism of debugging the external serial NOR flash with the RT MCU and illustrates the steps of modifying the flash driver algorithm of MCUXpresso. CoreSight Technical The i.MX RT series MCU is based on the Cortex-M core and the CoreSight Technical is a new debugging architecture launched by ARM in 2004 and is also a part of the core authorization, supports the debug and trace feature for Cortex-M core-based MCU. CoreSight is very powerful. It contains many debugging components (ie various protocols). The following figure is from the CoreSight Technical Introduction Manual, which shows the connections between various debugging components under the CoreSight architecture. Fig 1 CoreSight Technical This article does not mainly aim to introduce CoreSight technical. Therefore, for CoreSight, we only need to know that it in charge of the main debugging work and the CoreSight can access the system memory and peripheral register from the AMBA bus through the DAP component in real-time, definitely, it includes the code in the external serial Flash. FlexSPI module To implement debugging in serial Flash, the code must be XIP in serial Flash, that is, the CPU must be able to fetch instructions and data from any address in serial Flash in real-time. The serial Flash mentioned in this article generally refers to the 4-wire SPI Interface NOR Flash and the SPI mode can be Single/Dual/Quad/Octal. No matter which SPI mode is, the Flash is essentially serial Flash, and the address lines and data lines are not only shared but also serial. According to conventional knowledge, to implement the XIP, Flash should be a parallel bus interface and hung on AMBA, further, this parallel bus should have independent address lines and data lines, and the width of the address lines correspond to the size of Flash. So why can run XIP in serial Flash with i.MXRT? The answer is the FlexSPI peripheral. Figure 2 is the FlexSPI module block diagram. On the right side of the block diagram is the signal connection between FlexSPI and external serial Flash. The left side is the connection between FlexSPI and the internal bus of the i.MXRT system. There are two types of bus interface: 32bit IPS BUS (manual manipulate the FlexSPI register sends Flash reading and writing commands) and 64bit AHB BUS (FlexSPI translates the AHB access address and automatically sends the corresponding Flash reading and writing commands) which is the key feature enables the XIP available. Fig 2 FlexSPI module In the Reference manual, it lists detailed information about the AHB bus: - AHB RX Buffer implemented to reduce read latency. Total AHB RX Buffer size: 128 * 64 Bits - 16 AHB masters supported with priority for reading access - 4 flexible and configurable buffers in AHB RX Buffer - AHB TX Buffer implemented to buffer all write data from one AHB burst. AHB TX Buffer size: 8 * 64 Bits - All AHB masters share this AHB TX Buffer. No AHB master number limitation for Write Access. In addition, the AHB bus includes the below-enhanced features to optimize the reading of Serial Flash memory. - Cachable and Non-Cachable access - Prefetch Enable/Disable - Burst size: 8/16/32/64 bits - All burst type: SINGLE/INCR/WRAP4/INCR4/WRAP8/INCR8/WRAP16/INCR16 Debugging process of serial Flash Fig 3 illustrates the debugging process of serial Flash with the RT series MCU and in basic, the overview of the debugging process is not complicated. When you click IDE debugging icon, the Flash driver algorithm (executable file) pre-installed in the IDE will be downloaded to the internal FlexRAM of i.MXRT via the debugger firstly. The Flash driver algorithm provides FlexSPI initialization, erase and programming APIs, etc. Next, the debugger caches the application code (binary machine code) in FlexRAM in segments prior to calling the Flash programming API to implement the program work. After completing programming application code (from FlexRAM to Flash), CoreSight will take over the debugging work. At this time, the CPU can access the serial Flash that connects the FlexSPI module through the AHB bus, in another word, CoreSight can control and track code in real-time, and single-step debugging is available too in the IDE. Fig 3 Flash Driver of MCUXpresso IDE The latest version (24.12) of MCUXpresso IDE supports all i.Mx RT series MCU (as the following figure shows).   Fig 4 MCUXpresso IDE supported Parts The  developer should select a suitable flash driver file to apply to his board (Fig 5). Fig 5 Flash driver files   For more details about the flashdrivers supported by the MCUXpresso IDE, please refer to the MCUXpresso IDE  User Guide. Specifically check the two following sections : Flash drivers using SFDP (LPC and i.MX RT) and i.MX RT QSPI and Hyper Flash frivers   As mentioned above, the RT series MCUs don't have an internal flash, so they must use an either external parallel or serial NOR. For IDE providers, it's too hard to provide enough flash drivers to fit all external NOR flashes, the workload is huge, so IDEs general provide the flash driver files for mainstream Serial NOR, especially, 4-wire SPI Interface NOR Flash, it means we need to modify or tune the flash driver to fit our specific application. Add new flash driver of MCUXpresso IDE Before start, we should realize that MCUXpresso IDE is different from MDK/IAR. The flash driver algorithms of MDK and IAR are independent of the specific debug tools and they are able to use with all supported debug tools (JLink/DAPLink, etc). For MCUXpresso IDE, the flash driver algorithms are only able to use with the CMSIS-DAP type debug tool. For instance, when you use JLink with MCUXPresso IDE, it will use the flash driver algorithm of Jlink instead of its own. There's a real case from a customer: He currently designs his new card reader module based on RT1024 and he plans to make a board without external RAM and Flash. In other words, he only utilizes the internal 4MB flash and 256KB FlexRAM which consist of SRAM_DTC(64KB), SRAM_ITC(64KB), SRAM_OC(128KB). So he wants to configure the 256KB RAM area as normal 256KB RAM without being allocated to ITCM and DTCM. He follows the thread to reconfigure the FlexRAM, but he still encounters the below problem (as Fig 6 shows ) when entering debug mode. Fig 6 According to the debug failure log, we can come to a conclusion that the flash drive file: MIMXRT1020.cfx needs to be updated, and the following steps illustrate how to do it. a) Select a source project Flashdriver projects  on latest versions of the MCUXpresso IDE are delivered in Linkserver package. You can install Linkersever independently or during the MCUXPresso IDE installation. Therefore If you already installed MCUXpresso IDE you do not need to manually install the Linkserver, unless you want the latest version of Linkserver. If you are using MCUXpresso IDEv24 or above flashdriver projects can be found at  C:\nxp\LinkServer_24.12.21\Examples\Flashdrivers\NXP subdirectory within the MCUXpresso's Linkserver installation directory (as Fig 7 shows) and iMXRT folder contains some flash driver projects for external flash parts that work with the RT series MCU (as Fig 8 shows).   Fig 7   Fig 8 Select the flash driver project which is the closest to the target as a prototype, in this case, we select the iMXRT1020_QSPI project, extract the project file and import them in the MCUXpresso IDE (as Fig 9). Fig 9 b) Modify pin assignment The RT1024 integrates a 4 MB QSPI flash as an "internal flash", it is connected to different FlexSPI pins versus to the default pins of the iMXRT1020_QSPI project just as the below table shows. FlexSPI pin RT1020 RT1024 FLEXSPI_A_DQS GPIO_SD_B1_05 GPIO_SD_B1_05 FLEXSPI_A_SS0_B GPIO_SD_B1_11 GPIO_AD_B1_05 FLEXSPI_A_SCLK GPIO_SD_B1_07 GPIO_AD_B1_01 FLEXSPI_A_DATA0 GPIO_SD_B1_08 GPIO_AD_B1_02 FLEXSPI_A_DATA1 GPIO_SD_B1_10 GPIO_AD_B1_04 FLEXSPI_A_DATA2 GPIO_SD_B1_09 GPIO_AD_B1_03 FLEXSPI_A_DATA3 GPIO_SD_B1_06 GPIO_AD_B1_00 So it needs to adjust the pin initialization in the BOARD_InitPins() function in pin_mux.c. /* FUNCTION ************************************************************************************************************ * * Function Name : BOARD_InitPins * Description : Configures pin routing and optionally pin electrical features. * * END ****************************************************************************************************************/ void BOARD_InitPins(void) { CLOCK_EnableClock(kCLOCK_Iomuxc); /* iomuxc clock (iomuxc_clk_enable): 0x03u */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B0_06_LPUART1_TX, /* GPIO_AD_B0_06 is configured as LPUART1_TX */ 0U); /* Software Input On Field: Input Path is determined by functionality */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B0_07_LPUART1_RX, /* GPIO_AD_B0_07 is configured as LPUART1_RX */ 0U); /* Software Input On Field: Input Path is determined by functionality */ IOMUXC_SetPinMux( IOMUXC_GPIO_SD_B1_05_FLEXSPI_A_DQS, /* GPIO_SD_B1_05 is configured as FLEXSPI_A_DQS */ 1U); /* Software Input On Field: Force input path of pad GPIO_SD_B1_05 */ // IOMUXC_SetPinMux( // IOMUXC_GPIO_SD_B1_06_FLEXSPI_A_DATA03, /* GPIO_SD_B1_06 is configured as FLEXSPI_A_DATA03 */ // 1U); /* Software Input On Field: Force input path of pad GPIO_SD_B1_06 */ // IOMUXC_SetPinMux( // IOMUXC_GPIO_SD_B1_07_FLEXSPI_A_SCLK, /* GPIO_SD_B1_07 is configured as FLEXSPI_A_SCLK */ // 1U); /* Software Input On Field: Force input path of pad GPIO_SD_B1_07 */ // IOMUXC_SetPinMux( // IOMUXC_GPIO_SD_B1_08_FLEXSPI_A_DATA00, /* GPIO_SD_B1_08 is configured as FLEXSPI_A_DATA00 */ // 1U); /* Software Input On Field: Force input path of pad GPIO_SD_B1_08 */ // IOMUXC_SetPinMux( // IOMUXC_GPIO_SD_B1_09_FLEXSPI_A_DATA02, /* GPIO_SD_B1_09 is configured as FLEXSPI_A_DATA02 */ // 1U); /* Software Input On Field: Force input path of pad GPIO_SD_B1_09 */ // IOMUXC_SetPinMux( // IOMUXC_GPIO_SD_B1_10_FLEXSPI_A_DATA01, /* GPIO_SD_B1_10 is configured as FLEXSPI_A_DATA01 */ // 1U); /* Software Input On Field: Force input path of pad GPIO_SD_B1_10 */ // IOMUXC_SetPinMux( // IOMUXC_GPIO_SD_B1_11_FLEXSPI_A_SS0_B, /* GPIO_SD_B1_11 is configured as FLEXSPI_A_SS0_B */ // 1U); /* Software Input On Field: Force input path of pad GPIO_SD_B1_11 */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B1_00_FLEXSPI_A_DATA03, /* GPIO_AD_B1_00 is configured as FLEXSPI_A_DATA03 */ 1U); /* Software Input On Field: Force input path of pad GPIO_AD_B1_00 */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B1_01_FLEXSPI_A_SCLK, /* GPIO_AD_B1_01 is configured as FLEXSPI_A_SCLK */ 1U); /* Software Input On Field: Force input path of pad GPIO_AD_B1_01 */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B1_02_FLEXSPI_A_DATA00, /* GPIO_AD_B1_02 is configured as FLEXSPI_A_DATA00 */ 1U); /* Software Input On Field: Force input path of pad GPIO_AD_B1_02 */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B1_03_FLEXSPI_A_DATA02, /* GPIO_AD_B1_03 is configured as FLEXSPI_A_DATA02 */ 1U); /* Software Input On Field: Force input path of pad GPIO_AD_B1_03 */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B1_04_FLEXSPI_A_DATA01, /* GPIO_AD_B1_04 is configured as FLEXSPI_A_DATA01 */ 1U); /* Software Input On Field: Force input path of pad GPIO_AD_B1_04 */ IOMUXC_SetPinMux( IOMUXC_GPIO_AD_B1_05_FLEXSPI_A_SS0_B, /* GPIO_AD_B1_05 is configured as FLEXSPI_A_SS0_B */ 1U); /* Software Input On Field: Force input path of pad GPIO_AD_B1_05 */ IOMUXC_SetPinConfig( IOMUXC_GPIO_AD_B0_06_LPUART1_TX, /* GPIO_AD_B0_06 PAD functional properties : */ 0x10B0u); /* Slew Rate Field: Slow Slew Rate Drive Strength Field: R0/6 Speed Field: medium(100MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_AD_B0_07_LPUART1_RX, /* GPIO_AD_B0_07 PAD functional properties : */ 0x10B0u); /* Slew Rate Field: Slow Slew Rate Drive Strength Field: R0/6 Speed Field: medium(100MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_SD_B1_05_FLEXSPI_A_DQS, /* GPIO_SD_B1_05 PAD functional properties : */ 0x10F1u); /* Slew Rate Field: Fast Slew Rate Drive Strength Field: R0/6 Speed Field: max(200MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_SD_B1_06_FLEXSPI_A_DATA03, /* GPIO_SD_B1_06 PAD functional properties : */ 0x10F1u); /* Slew Rate Field: Fast Slew Rate Drive Strength Field: R0/6 Speed Field: max(200MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_SD_B1_07_FLEXSPI_A_SCLK, /* GPIO_SD_B1_07 PAD functional properties : */ 0x10F1u); /* Slew Rate Field: Fast Slew Rate Drive Strength Field: R0/6 Speed Field: max(200MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_SD_B1_08_FLEXSPI_A_DATA00, /* GPIO_SD_B1_08 PAD functional properties : */ 0x10F1u); /* Slew Rate Field: Fast Slew Rate Drive Strength Field: R0/6 Speed Field: max(200MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_SD_B1_09_FLEXSPI_A_DATA02, /* GPIO_SD_B1_09 PAD functional properties : */ 0x10F1u); /* Slew Rate Field: Fast Slew Rate Drive Strength Field: R0/6 Speed Field: max(200MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_SD_B1_10_FLEXSPI_A_DATA01, /* GPIO_SD_B1_10 PAD functional properties : */ 0x10F1u); /* Slew Rate Field: Fast Slew Rate Drive Strength Field: R0/6 Speed Field: max(200MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ IOMUXC_SetPinConfig( IOMUXC_GPIO_SD_B1_11_FLEXSPI_A_SS0_B, /* GPIO_SD_B1_11 PAD functional properties : */ 0x10F1u); /* Slew Rate Field: Fast Slew Rate Drive Strength Field: R0/6 Speed Field: max(200MHz) Open Drain Enable Field: Open Drain Disabled Pull / Keep Enable Field: Pull/Keeper Enabled Pull / Keep Select Field: Keeper Pull Up / Down Config. Field: 100K Ohm Pull Down Hyst. Enable Field: Hysteresis Disabled */ } c) Modify linker file According to Fig 3, a flash driver should be downloaded into FlexRAM on the target MCU during the debuggingprocess, for the iMXRT1020_QSPI project, the flash driver needs to be downloaded to DTCM (0x2000_0000~0x2001_0000), however, to meet the customer's demand, the whole of FlexRAM is reconfigured to SRAM_OC in the ResetISR() function. In another word, there's no DTCM area to load the flash driver and it causes the above debug failure. So we need to use the SRAM_OC instead of DTCM to load the flash driver just like the below shows. In the FlashDriver_32Kbuffer.ld of iMXRT1020_QSPI project: /* * Linker script for NXP LPC546xx SPIFI Flash Driver (Messaged) */ MEMORY { /*SRAM (rwx) : ORIGIN = 0x20000000, LENGTH = (64 * 1024)*/ SRAM (rwx) : ORIGIN = 0x20200000, LENGTH = (64 * 1024) } /* stack size : multiple of 8*/ __stack_size = (4 * 1024); /* flash image buffer size : multiple of page size*/ __cache_size = (32 * 1024); /* Supported operations bit map * 0x40 = New device info available after Init() call * This setting must match the actual target flash driver build! */ __opmap_val = 0x1000; /* Actual placement of flash driver code/data controlled via standard file */ INCLUDE "../../LPCXFlashDriverLib/linker/placement.ld" d) Recompile In the LPCXFlashDriverLib project, select the Release_SectorHashing option prior to clicking the Build icon to generate libLPCXFlashDriverLib.a file (as Fig 10 shows). Fig 10 Next, in the iMXRT1020_QSPI project, select the MIMXRT1020-EVK_IS25LP064 option (as Fig 11 shows), then click the Build icon to generate a new flash driver file that resides in ~\Examples\Flashdrivers\NXP\iMXRT\iMXRT1020_QSPI\iMXRT1020_QSPI\builds directory. Fig 11 Note: I've attached a test project which is based on the hello_world demo that comes from the RT1024's SDK library, in addition, the attachment also contains the new flash driver and corresponding debug script files, so please give it a try.   Debug the  flash driver of MCUXpresso IDE As mentioned on other parts of this document you could change the pin mux assignments and edit the flashdriver. However, before testing your custom flash driver you could do a simple debug. All the flashdriver projects come with a debug build configuration.  Make sure debug build configuration is enabled as shown in Fig 12 Fig 12  After enabling debug build you can simply trigger a standard debug operation, and the IDE will load into SRAM a simple test code. The test code will detect the flash and perform an erase procedure. See Fig 13 Fig 13 In the terminal you will see the output log from the test program.       Edit changes by @diego_charles : Update Fig 7, Fig 8 and a) Select source project , updated  Flash Driver of MCUXpresso IDE section, updated Flash Driver of MCUXpresso IDE section, fig 4, added  Debug the flash Driver of MCUXpresso IDE section    
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RT10xx SAI basic and SDCard wave file play 1. Introduction NXP RT10xx's audio modules are SAI, SPDIF, and MQS. The SAI module is a synchronous serial interface for audio data transmission. SPDIF is a stereo transceiver that can receive and send digital audio, MQS is used to convert I2S audio data from SAI3 to PWM, and then can drive external speakers, but in practical usage, it still need to add the amplifier drive circuit. When we use the SAI module, it will be related to the audio file play and the data obtained. This article will be based on the MIMXRT1060-EVK board, give the RT10xx SAI module basic knowledge, PCM waveform format, the audio file cut, and conversion tool, use the MCUXpresso IDE CFG peripheral tool to create the SAI project, play the audio data, it will also provide the SDcard with fatfs system to read the wave file and play it. 2. Basic Knowledge and the tools Before entering the project details and testing, just provide some SAI module knowledge, wave file format information, audio convert tools. 2.1 SAI module basic RT10xx SAI module can support I2S, AC97, TDM, and codec/DSP interface. SAI module contains Transmitter and Receiver, the related signals:     SAI_MCLK: master clock, used to generate the bit clock, master output, slave input.     SAI_TX_BCLK: Transmit bit clock, master output, slave input     SAI_TX_SYNC: Transmit Frame sync, master output, slave input, L/R channel select     SAI_TX_DATA[4]:Transmit data line, 1-3 share with RX_DATA[1-3]     SAI_RX_BCLK: receiver bit clock     SAI_RX_SYNC: receiver frame sync     SAI_RX_DATA[4]: receiver data line SAI module clocks: audio master clock, bus clock, bit clock SAI module Frame sync has 3 modes:      1)Transmit and receive using its own BCLK and SYNC      2)Transmit async, receive sync: use transmit BCLK and SYNC, transmit enable at first, disable at last.      3)Transmit sync, receive async: use receive BCLK and SYNC, receiver enable at first, disable at last. Valid frame sync is also ignored (slave mode) or not generated (master mode) for the first four-bit clock cycles after enabling the transmitter or receiver. Pic 1 SAI module clock structure: Pic 2 SAI module 3 clock sources:  PLL3_PFD3, PLL5, PLL4 In the above picture, SAI1_CLK_ROOT, which can be used as the MCLK, the BCLK is: BCLK= master clock/(TCR2[DIV]+1)*2 Sample rate = Bitclockfreq /(bitwidth*channel) 2.2 waveform audio file format WAVE file is used to save the PCM encode data, WAVE is using the RIFF format, the smallest unit in the RIFF file is the CK struct, CKID is the data type, the value can be: “RIFF”,“LIST”,“fmt”, “data” etc. RIFF file is little-endian. RIFF structure: typedef unsigned long DWORD;//4B typedef unsigned char BYTE;//1B typedef DWORD FOURCC; // 4B typedef struct { FOURCC ckID; //4B DWORD ckSize; //4B union { FOURCC fccType; // RIFF form type 4B BYTE ckData[ckSize]; //ckSize*1B } ckData; } RIFFCK; Pic 3 Take a 16Khz 2 channel wave file as the example: Pic 4 Yellow: CKID  Green: data length   Purple: data The detailed analysis as follows: Pic 5 We can find, the real audio data, except the wave header, the data size is 1279860bytes. 2.3 Audio file convert In practical usage, the audio file may not the required channel and the sample rate configuration, or the format is not the wave, or the time is too long, then we can use some tool to convert it to your desired format. We can use the ffmpeg tool: https://ffmpeg.org/ About the details, check the ffmpeg document, normally we use these command: mp3 file converts to 16k, 16bit, 2 channel wave file: ffmpeg -i test.mp3 -acodec pcm_s16le -ar 16000 -ac 2 test.wav or: ffmpeg -i test.mp3 -aq 16 -ar 16000 -ac 2 test.wav test.wav, cut 35s from 00:00:00, and can convert save to test1.wav: ffmpeg -ss 00:00:00 -i test.wav -t 35.0 -c copy test1.wav Pic 6 Pic 7 2.4 Obtain wave L/R channel audio data Just like the SDK code, save the L/R audio data directly in the RT RAM array, so here, we need to obtain the audio data from the wav file. We can use the python readout the wav header, then get the audio data size, and save the audio data to one array in the .h files. The related Python code can be: import sys import wave def wav2hex(strWav, strHex): with wave.open(strWav, "rb") as fWav: wavChannels = fWav.getnchannels() wavSampleWidth = fWav.getsampwidth() wavFrameRate = fWav.getframerate() wavFrameNum = fWav.getnframes() wavFrames = fWav.readframes(wavFrameNum) wavDuration = wavFrameNum / wavFrameRate wafFramebytes = wavFrameNum * wavChannels * wavSampleWidth print("Channels: {}".format(wavChannels)) print("Sample width: {}bits".format(wavSampleWidth * 8)) print("Sample rate: {}kHz".format(wavFrameRate/1000)) print("Frames number: {}".format(wavFrameNum)) print("Duration: {}s".format(wavDuration)) print("Frames bytes: {}".format(wafFramebytes)) fWav.close() pass with open(strHex, "w") as fHex: # Print WAV parameters fHex.write("/*\n"); fHex.write(" Channels: {}\n".format(wavChannels)) fHex.write(" Sample width: {}bits\n".format(wavSampleWidth * 8)) fHex.write(" Sample rate: {}kHz\n".format(wavFrameRate/1000)) fHex.write(" Frames number: {}\n".format(wavFrameNum)) fHex.write(" Duration: {}s\n".format(wavDuration)) fHex.write(" Frames bytes: {}\n".format(wafFramebytes)) fHex.write("*/\n\n") # Print WAV frames fHex.write("uint8_t music[] = {\n") print("Transferring...") i = 0 while wafFramebytes > 0: if(wafFramebytes < 16): BytesToPrint = wafFramebytes else: BytesToPrint = 16 fHex.write(" ") for j in range(0, BytesToPrint): if j != 0: fHex.write(' ') fHex.write("0x{:0>2x},".format(wavFrames[i])) i+=1 j+=1 fHex.write("\n") wafFramebytes -= BytesToPrint fHex.write("};\n") fHex.close() print("Done!") wav2hex(sys.argv[1], sys.argv[2]) Take the music1.wave as an example: Pic 8 2.4 Audio data relationship with audio wave 16bit data range is: -32768 to 32767, the goldwave related value range is(-1~1).Use goldwave tool to open the example music1.wav, check the data in 1s position, the left channel relative data is -0.08227, right channel relative data is -0.2257. Pic 9                                                                          pic 10 Now, calculate the L/R real data, and find the position in the music1.h. Pic 11 From pic 8, we can know, the real wave R/L data from line 11, each line contains 16 bytes of data. So, from music1.wav related value, we can calculate the related data, and compare it with the real data in the array, we can find, it is totally the same. 3. SAI MCUXpresso project creation Based on SDK_2.9.2_EVK-MIMXRT1060, create one SAI DMA audio play project. The audio data can use the above music1.h. Create one bare-metal project: Drivers check: clock, common, dmamux, edma,gpio,i2c,iomuxc,lpuart,sai,sai_edma,xip_device Utilities check:       Debug_console,lpuart_adapter,serial_manager,serial_manager_uart Board components check:       Xip_board Abstraction Layer check:       Codec, codec_wm8960_adapter,lpi2c_adapter Software Components check:       Codec_i2c,lists,wm8960 After the creation of the project, open the clocks, configure the clock, core, flexSPI can use the default one, we mainly configure the SAI1 related clocks: Pic 12 Select the SAI1 clock source as PLL4, PLL4_MAIN_CLK configure as 786.48MHz. SAI1 clock configure as 6.144375MHz. After the configuration, update the code. Open Pins tool, configure the SAI1 related pins, as the codec also need the I2C, so it contains the I2C pin configuration. Pic 13 Update the code. Open peripherals, configure DMA, SAI, NVIC. Pic 14 Pic 15 DMA配置如下: pic16 After configuration, generate the code. In the above configuration, we have finished the SAI DMA transfer configuration, SAI master mode, 16bits, the sample rate is 16kHz, 2channel, DMA transfer, bit clock is 512Khz, the master clock is 6.1443Mhz. void callback(I2S_Type *base, sai_edma_handle_t *handle, status_t status, void *userData) { if (kStatus_SAI_RxError == status) { } else { finishIndex++; emptyBlock++; /* Judge whether the music array is completely transfered. */ if (MUSIC_LEN / BUFFER_SIZE == finishIndex) { isFinished = true; finishIndex = 0; emptyBlock = BUFFER_NUM; tx_index = 0; cpy_index = 0; } } } int main(void) { sai_transfer_t xfer; /* Init board hardware. */ BOARD_ConfigMPU(); BOARD_InitBootPins(); BOARD_InitBootClocks(); BOARD_InitBootPeripherals(); #ifndef BOARD_INIT_DEBUG_CONSOLE_PERIPHERAL /* Init FSL debug console. */ BOARD_InitDebugConsole(); #endif PRINTF(" SAI wav module test!\n\r"); /* Use default setting to init codec */ if (CODEC_Init(&codecHandle, &boardCodecConfig) != kStatus_Success) { assert(false); } /* delay for codec output stable */ DelayMS(DEMO_CODEC_INIT_DELAY_MS); CODEC_SetVolume(&codecHandle,2U,50); // set 50% volume EnableIRQ(DEMO_SAI_IRQ); SAI_TxEnableInterrupts(DEMO_SAI, kSAI_FIFOErrorInterruptEnable); PRINTF(" MUSIC PLAY Start!\n\r"); while (1) { PRINTF(" MUSIC PLAY Again\n\r"); isFinished = false; while (!isFinished) { if ((emptyBlock > 0U) && (cpy_index < MUSIC_LEN / BUFFER_SIZE)) { /* Fill in the buffers. */ memcpy((uint8_t *)&buffer[BUFFER_SIZE * (cpy_index % BUFFER_NUM)], (uint8_t *)&music[cpy_index * BUFFER_SIZE], sizeof(uint8_t) * BUFFER_SIZE); emptyBlock--; cpy_index++; } if (emptyBlock < BUFFER_NUM) { /* xfer structure */ xfer.data = (uint8_t *)&buffer[BUFFER_SIZE * (tx_index % BUFFER_NUM)]; xfer.dataSize = BUFFER_SIZE; /* Wait for available queue. */ if (kStatus_Success == SAI_TransferSendEDMA(DEMO_SAI, &SAI1_SAI_Tx_eDMA_Handle, &xfer)) { tx_index++; } } } } }   4. SAI test result     To check the real L/R data sendout situation, we modify the music array first 16 bytes data as: 0x55,0xaa,0x01,0x00,0x02,0x00,0x03,0x00,0x04,0x00,0x05,0x00,0x06,0x00,0x07,0x00 Then test SAI_MCLK,SAI_TX_BCLK,SAI_TX_SYNC,SAI_TXD pin wave, and compare with the defined data, because the polarity is configured as active low, it is falling edge output, sample at rising edge. The test point on the MIMXRT1060-EVK board is using the codec pin position: Pic 17 4.1 Logic Analyzer tool wave Pic 18 MCLK clock frequency is 6.144375Mhz, BCLK is 512KHz, SYNC is 16KHz. Pic 19 The first frame data is:1010101001010101 0000000000000001 0XAA55  0X0001 It is the same as the array defined L/R data. SYNC low is Left 16 bit, High is right 16 bit. 4.2 Oscilloscope test wave Just like the logic analyzer, the oscilloscope wave is the same: Pic 20 Add the music.h to the project, and let the main code play the music array data in loop, we will hear the music clear when insert the headphone to on board J12 or add a speaker. 5. SAI SDcard wave music play This part will add the sd card, fatfs system, to read out the 16bit 16K 2ch wave file in the sd card, and play it in loop. 5.1 driver add     Code is based on SDK_2.9.2_EVK-MIMXRT1060, just on the previous project, add the sdcard, sd fatfs driver, now the bare-metal driver situation is: Drivers check: cache, clock, common, dmamux, edma,gpio,i2c,iomuxc,lpuart,sai,sai_edma,sdhc, xip_device Utilities check:       Debug_console,lpuart_adapter,serial_manager,serial_manager_uart Middleware check:       File System->FAT File System->fatfs+sd, Memories Board components check:       Xip_board Abstraction Layer check:       Codec, codec_wm8960_adapter,lpi2c_adapter Software Components check:       Codec_i2c,lists,wm8960 5.2 WAVE header analyzer with code    From previous content, we can know the wav header structure, we need to play the wave file from the sd card, then we need to analyze the wave header to get the audio format, audio data-related information. The header analysis code is: uint8_t Fun_Wave_Header_Analyzer(void) { char * datap; uint8_t ErrFlag = 0; datap = strstr((char*)Wav_HDBuffer,"RIFF"); if(datap != NULL) { wav_header.chunk_size = ((uint32_t)*(Wav_HDBuffer+4)) + (((uint32_t)*(Wav_HDBuffer + 5)) << + (((uint32_t)*(Wav_HDBuffer + 6)) << 16) +(((uint32_t)*(Wav_HDBuffer + 7)) << 24); movecnt += 8; } else { ErrFlag = 1; return ErrFlag; } datap = strstr((char*)(Wav_HDBuffer+movecnt),"WAVEfmt"); if(datap != NULL) { movecnt += 8; wav_header.fmtchunk_size = ((uint32_t)*(Wav_HDBuffer+movecnt+0)) + (((uint32_t)*(Wav_HDBuffer +movecnt+ 1)) << + (((uint32_t)*(Wav_HDBuffer +movecnt+ 2)) << 16) +(((uint32_t)*(Wav_HDBuffer +movecnt+ 3)) << 24); wav_header.audio_format = ((uint16_t)*(Wav_HDBuffer+movecnt+4) + (uint16_t)*(Wav_HDBuffer+movecnt+5)); wav_header.num_channels = ((uint16_t)*(Wav_HDBuffer+movecnt+6) + (uint16_t)*(Wav_HDBuffer+movecnt+7)); wav_header.sample_rate = ((uint32_t)*(Wav_HDBuffer+movecnt+8)) + (((uint32_t)*(Wav_HDBuffer +movecnt+ 9)) << + (((uint32_t)*(Wav_HDBuffer +movecnt+ 10)) << 16) +(((uint32_t)*(Wav_HDBuffer +movecnt+ 11)) << 24); wav_header.byte_rate = ((uint32_t)*(Wav_HDBuffer+movecnt+12)) + (((uint32_t)*(Wav_HDBuffer +movecnt+ 13)) << + (((uint32_t)*(Wav_HDBuffer +movecnt+ 14)) << 16) +(((uint32_t)*(Wav_HDBuffer +movecnt+ 15)) << 24); wav_header.block_align = ((uint16_t)*(Wav_HDBuffer+movecnt+16) + (uint16_t)*(Wav_HDBuffer+movecnt+17)); wav_header.bps = ((uint16_t)*(Wav_HDBuffer+movecnt+18) + (uint16_t)*(Wav_HDBuffer+movecnt+19)); movecnt +=(4+wav_header.fmtchunk_size); } else { ErrFlag = 1; return ErrFlag; } datap = strstr((char*)(Wav_HDBuffer+movecnt),"LIST"); if(datap != NULL) { movecnt += 4; wav_header.list_size = ((uint32_t)*(Wav_HDBuffer+movecnt+0)) + (((uint32_t)*(Wav_HDBuffer +movecnt+ 1)) << + (((uint32_t)*(Wav_HDBuffer +movecnt+ 2)) << 16) +(((uint32_t)*(Wav_HDBuffer +movecnt+ 3)) << 24); movecnt +=(4+wav_header.list_size); } //LIST not Must datap = strstr((char*)(Wav_HDBuffer+movecnt),"data"); if(datap != NULL) { movecnt += 4; wav_header.datachunk_size = ((uint32_t)*(Wav_HDBuffer+movecnt+0)) + (((uint32_t)*(Wav_HDBuffer +movecnt+ 1)) << + (((uint32_t)*(Wav_HDBuffer +movecnt+ 2)) << 16) +(((uint32_t)*(Wav_HDBuffer +movecnt+ 3)) << 24); movecnt += 4; ErrFlag = 0; } else { ErrFlag = 1; return ErrFlag; } PRINTF("Wave audio format is %d\r\n",wav_header.audio_format); PRINTF("Wave audio channel number is %d\r\n",wav_header.num_channels); PRINTF("Wave audio sample rate is %d\r\n",wav_header.sample_rate); PRINTF("Wave audio byte rate is %d\r\n",wav_header.byte_rate); PRINTF("Wave audio block align is %d\r\n",wav_header.block_align); PRINTF("Wave audio bit per sample is %d\r\n",wav_header.bps); PRINTF("Wave audio data size is %d\r\n",wav_header.datachunk_size); return ErrFlag; } Mainly divide RIFF to 4 parts: “RIFF”,“fmt”,“LIST”,“data”. The 4 bytes data follows the “data” is the whole audio data size, it can be used to the fatfs to read the audio data. The above code also recodes the data position, then when using the fatfs read the wave, we can jump to the data area directly. 5.3 SD card wave data play     Define the array audioBuff[4* 512], used to read out the sd card wave file, and use these data send to the SAI EDMA and transfer it to the I2S interface until all the data is transmitted to the I2S interface.     Callback record each 512 bytes data send out finished, and judge the transmit data size is reached the whole wave audio data size. 5.4 sd card wave play result    Prepare one wave file, 16bit 16k sample rate, 2 channel file, named as music.wav, put in the sd card which already does the fat32 format, insert it to the MIMXRT1060-EVK J39, run the code, will get the printf information: Please insert a card into the board. Card inserted. Make file system......The time may be long if the card capacity is big. SAI wav module test! MUSIC PLAY Start! Wave audio format is 1 Wave audio channel number is 2 Wave audio sample rate is 16000 Wave audio byte rate is 64000 Wave audio block align is 4 Wave audio bit per sample is 16 Wave audio data size is 2728440 Playback is begin! Playback is finished! At the same time, after inserting the headphone or the speaker into the J12, we can hear the music. Attachment is the mcuxpresso10.3.0 and the wave samples.  
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[中文翻译版] 见附件   原文链接: https://community.nxp.com/t5/i-MX-RT-Knowledge-Base/Design-an-IoT-edge-node-for-CV-application-base-on-the-i/ta-p/1127423 
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[中文翻译版] 见附件   原文链接: https://community.nxp.com/t5/i-MX-Community-Articles/Effortless-GUI-Development-with-NXP-Microcontrollers/ba-p/1131179  
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[中文翻译版] 见附件   原文链接: https://community.nxp.com/docs/DOC-345190  
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[中文翻译版] 见附件   原文链接: https://community.nxp.com/t5/eIQ-Machine-Learning-Software/eIQ-on-i-MX-RT1064-EVK/ta-p/1123602 
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[中文翻译版] 见附件   原文链接: https://community.nxp.com/t5/i-MX-RT-Knowledge-Base/RT1050-HAB-Encrypted-Image-Generation-and-Analysis/ta-p/1124877  
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In the tutorial, I'd like to show the steps of deploying an image classification model on i.MX RT1060 to enabling you to classify fashion images and categories. In the first part of this tutorial, we will review the Fashion MNIST dataset, including how to download it to your system. From there we’ll define a simple CNN network using the TensorFlow platform. Next, we’ll train our CNN model on the Fashion MNIST dataset, train it, and review the results. Finally, we'll optimize the model, after that, the model will be smaller and increase inferencing speed, which is valuable for source-limited devices such as MCU. Let’s go ahead and get started! Fashion MNIST dataset The Fashion MNIST dataset was created by the e-commerce company, Zalando. Fig 1 Fashion MNIST dataset As they note on their official GitHub repo for the Fashion MNIST dataset, there are a few problems with the standard MNIST digit recognition dataset: It’s far too easy for standard machine learning algorithms to obtain 97%+ accuracy. It’s even easier for deep learning models to achieve 99%+ accuracy. The dataset is overused. MNIST cannot represent modern computer vision tasks. Zalando, therefore, created the Fashion MNIST dataset as a drop-in replacement for MNIST. 60,000 training examples 10,000 testing examples 10 classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot 28×28 grayscale images The code below loads the Fashion-MNIST dataset using the TensorFlow and creates a plot of the first 25 images in the training dataset. import tensorflow as tf import numpy as np # For easy reset of notebook state. tf.keras.backend.clear_session() # load dataset fashion_mnist = tf.keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() lass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] plt.figure(figsize=(8,8)) for i in range(25): plt.subplot(5,5,i+1,) plt.tight_layout() plt.imshow(train_images[i]) plt.xlabel(lass_names[train_labels[i]]) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.show() Fig 2 Running the code loads the Fashion-MNIST train and test dataset and prints their shape. Fig 3 We can see that there are 60,000 examples in the training dataset and 10,000 in the test dataset and that images are indeed square with 28×28 pixels. Creating model We need to define a neural network model for the image classify purpose, and the model should have two main parts: the feature extraction and the classifier that makes a prediction. Defining a simple Convolutional Neural Network (CNN) For the convolutional front-end, we build 3 layers of convolution layer with a small filter size (3,3) and a modest number of filters followed by a max-pooling layer. The last filter map is flattened to provide features to the classifier. As we know, it's a multi-class classification task, so we will require an output layer with 10 nodes in order to predict the probability distribution of an image belonging to each of the 10 classes. In this case, we will require the use of a softmax activation function. And between the feature extractor and the output layer, we can add a dense layer to interpret the features. All layers will use the ReLU activation function and the He weight initialization scheme, both best practices. We will use the Adam optimizer to optimize the sparse_categorical_crossentropy loss function, suitable for multi-class classification, and we will monitor the classification accuracy metric, which is appropriate given we have the same number of examples in each of the 10 classes. The below code will define and run it will show the struct of the model. # Define a Model model = tf.keras.models.Sequential() # First Convolution ,Kernel:16*3*3 model.add( tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_uniform',input_shape=(28, 28, 1))) model.add( tf.keras.layers.MaxPooling2D((2, 2))) # Second Convolution ,Kernel:32*3*3 model.add( tf.keras.layers.Conv2D(32, (3, 3), activation='relu',kernel_initializer='he_uniform')) model.add( tf.keras.layers.MaxPooling2D((2, 2))) # Third Convolution ,Kernel:32*3*3 model.add( tf.keras.layers.Conv2D(32, (3, 3), activation='relu',kernel_initializer='he_uniform')) model.add( tf.keras.layers.Flatten()) model.add( tf.keras.layers.Dense(32, activation='relu',kernel_initializer='he_uniform')) model.add( tf.keras.layers.Dense(10, activation='softmax')) Fig 4 Training Model After the model is defined, we need to train it. The model will be trained using 5-fold cross-validation. The value of k=5 was chosen to provide a baseline for both repeated evaluation and to not be too large as to require a long running time. Each validation set will be 20% of the training dataset or about 12,000 examples. The training dataset is shuffled prior to being split and the sample shuffling is performed each time so that any model we train will have the same train and validation datasets in each fold, providing an apples-to-apples comparison. We will train the baseline model for a modest 20 training epochs with a default batch size of 32 examples. The validation set for each fold will be used to validate the model during each epoch of the training run, so we can later create learning curves, and at the end of the run, we use the test dataset to estimate the performance of the model. As such, we will keep track of the resulting history from each run, as well as the classification accuracy of the fold. The train_model() function below implements these behaviors, taking the training dataset and test dataset as arguments, and returning a list of accuracy scores and training histories that can be later summarized. from sklearn.model_selection import KFold # train a model using k-fold cross-validation def train_model(dataX, dataY, n_folds=5): scores, histories = list(), list() # prepare cross validation kfold = KFold(n_folds, shuffle=True, random_state=1) for train_ix, validate_ix in kfold.split(dataX): # select rows for train and test trainX, trainY, validate_X, validate_Y = dataX[train_ix], dataY[train_ix], dataX[validate_ix], dataY[validate_ix] # fit model history = model.fit(trainX, trainY, epochs=20, batch_size=32, validation_data=(validate_X, validate_Y), verbose=0) # evaluate model _, acc = model.evaluate(validate_X, validate_Y, verbose=0) print("Accurary: {:.4f},Total number of figures is {:0>2d}".format(acc * 100.0, len(testY))) # append scores scores.append(acc) histories.append(history) return scores, histories Module Summary After the model has been trained, we can present the results. There are two key aspects to present: the diagnostics of the learning behavior of the model during training and the estimation of the model performance. These can be implemented using separate functions. First, the diagnostics involve creating a line plot showing model performance on the train and validate set during each fold of the k-fold cross-validation. These plots are valuable for getting an idea of whether a model is overfitting, underfitting, or has a good fit for the dataset. We will create a single figure with two subplots, one for loss and one for accuracy. Blue lines will indicate model performance on the training dataset and orange lines will indicate performance on the hold-out validate dataset. The summarize_diagnostics() function below creates and shows this plot given the collected training histories. # plot diagnostic learning curves def summarize_diagnostics(histories): for i in range(len(histories)): # plot loss plt.subplot(2,1,1) plt.title('Cross Entropy Loss') plt.plot(histories[i].history['loss'], color='blue', label='train') plt.plot(histories[i].history['val_loss'], color='orange', label='test') # plot accuracy plt.subplot(2,1,2) plt.title('Classification Accuracy') plt.plot(histories[i].history['accuracy'], color='blue', label='train') plt.plot(histories[i].history['val_accuracy'], color='orange', label='test') plt.show() Fig 5 Next, the classification accuracy scores collected during each fold can be summarized by calculating the mean and standard deviation. This provides an estimate of the average expected performance of the model trained on the test dataset, with an estimate of the average variance in the mean. We will also summarize the distribution of scores by creating and showing a box and whisker plot. The summarize_performance() function below implements this for a given list of scores collected during model training. # summarize model performance def summarize_performance(scores): # print summary print('Accuracy: mean={:.4f} std={:.4f}, n={:0>2d}'.format(np.mean(trained_scores)*100, np.std(trained_scores)*100, len(scores))) # box and whisker plots of results plt.boxplot(scores) plt.show()   Fig 6 Verifying predictions According to the above figure, we see that the final trained model can get up to around 87.6% accuracy when predicting the test dataset. And with the trained model, running the below code will demonstrate the result of predictions about some images. def plot_image(i, predictions_array, true_label, img): true_label, img = true_label[i], img[i] plt.grid(False) plt.xticks([]) plt.yticks([]) plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary) predicted_label = np.argmax(predictions_array) if predicted_label == true_label: color = 'blue' else: color = 'red' plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label], 100*np.max(predictions_array), class_names[true_label]), color=color) def plot_value_array(i, predictions_array, true_label): true_label = true_label[i] plt.grid(False) plt.xticks(range(10)) plt.yticks([]) thisplot = plt.bar(range(10), predictions_array, color="#777777") plt.ylim([0, 1]) predicted_label = np.argmax(predictions_array) thisplot[predicted_label].set_color('red') thisplot[true_label].set_color('blue') predictions = model.predict(test_images) # Plot the first X test images, their predicted labels, and the true labels. # Color correct predictions in blue and incorrect predictions in red. num_rows = 5 num_cols = 3 num_images = num_rows*num_cols plt.figure(figsize=(2*2*num_cols, 2*num_rows)) for i in range(num_images): plt.subplot(num_rows, 2*num_cols, 2*i+1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(num_rows, 2*num_cols, 2*i+2) plot_value_array(i, predictions[i], test_labels) plt.tight_layout() plt.show()   Fig 7 Model quantization Post-training quantization is a conversion technique that can reduce model size while also improving CPU and hardware accelerator latency, with little degradation in model accuracy, especially it's crucial to embedded platforms, as it lacks the compute-intensive performance, the Flash and RAM memory is also very limited. TensorFlow Lite is able to be used to convert an already-trained float TensorFlow model to the TensorFlow Lite format. In addition, the TensorFlow Lite provides several approaches to optimize the mode, among these ways, Integer quantization is an optimization strategy that converts 32-bit floating-point numbers (such as weights and activation outputs) to the nearest 8-bit fixed-point numbers. This results in a smaller model and increased inferencing speed, which is very valuable for low-power devices such as microcontrollers. The below codes show how to implement the Integer quantization of the trained model, and after running these codes, we can find that the size of Tensorflow Lite mode reduces almost 64.9 KB versus the original model, becomes about 32% of the original size(Fig 8). import os # Convert using integer-only quantization def representative_data_gen(): for input_value in tf.data.Dataset.from_tensor_slices(tf.cast(train_images,tf.float32)).shuffle(500).batch(1).take(150): yield [input_value] # Convert using dynamic range quantization converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.optimizations = [tf.lite.Optimize.DEFAULT] tflite_model_quant = converter.convert() # Save the model to disk open("model_dynamic_range_quantization.tflite", "wb").write(tflite_model_quant) ## Size difference Dynamic_range_quantization_model_size = os.path.getsize("model_dynamic_range_quantization.tflite") print("Dynamic range quantization model is %d bytes" % Dynamic_range_quantization_model_size) converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = representative_data_gen # Ensure that if any ops can't be quantized, the converter throws an error converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] # Set the input and output tensors to uint8 (APIs added in r2.3) converter.inference_input_type = tf.uint8 converter.inference_output_type = tf.uint8 tflite_model_advanced_quant = converter.convert() # Save the model to disk open("model_integer_only_quantization.tflite", "wb").write(tflite_model_advanced_quant) Integer_only_quantization_model_size = os.path.getsize("model_integer_only_quantization.tflite") print("Integer_only_quantization_model is %d bytes" % Integer_only_quantization_model_size) difference = Dynamic_range_quantization_model_size - Integer_only_quantization_model_size print("Difference is %d bytes" % difference) Fig 8 Evaluating the TensorFlow Lite model Now we'll run inferences using the TensorFlow Lite Interpreter to compare the model accuracies. First, we need a function that runs inference with a given model and images, and then returns the predictions: # Helper function to run inference on a TFLite model def run_tflite_model(tflite_file, test_image_indices): # Initialize the interpreter interpreter = tf.lite.Interpreter(model_path=str(tflite_file)) interpreter.allocate_tensors() input_details = interpreter.get_input_details()[0] output_details = interpreter.get_output_details()[0] predictions = np.zeros((len(test_image_indices),), dtype=int) for i, test_image_index in enumerate(test_image_indices): test_image = test_images[test_image_index] test_label = test_labels[test_image_index] # Check if the input type is quantized, then rescale input data to uint8 if input_details['dtype'] == np.uint8: input_scale, input_zero_point = input_details["quantization"] test_image = test_image / input_scale + input_zero_point test_image = np.expand_dims(test_image, axis=0).astype(input_details["dtype"]) interpreter.set_tensor(input_details["index"], test_image) interpreter.invoke() output = interpreter.get_tensor(output_details["index"])[0] predictions[i] = output.argmax() return predictions Next, we'll compare the performance of the original model and the quantized model on one image. model_basic_quantization.tflite is the original TensorFlow Lite model with floating-point data. model_integer_only_quantization.tflite is the last model we converted using integer-only quantization (it uses uint8 data for input and output). Let's create another function to print our predictions and run it for testing. import matplotlib.pylab as plt # Change this to test a different image test_image_index = 1 ## Helper function to test the models on one image def test_model(tflite_file, test_image_index, model_type): global test_labels predictions = run_tflite_model(tflite_file, [test_image_index]) plt.imshow(test_images[test_image_index].reshape(28,28)) template = model_type + " Model \n True:{true}, Predicted:{predict}" _ = plt.title(template.format(true= str(test_labels[test_image_index]), predict=str(predictions[0]))) plt.grid(False) Fig 9 Fig 10 Then evaluate the quantized model by using all the test images we loaded at the beginning of this tutorial. After summarizing the prediction result of the test dataset, we can see that the prediction accuracy of the quantized model decrease 7% less than the original model, it's not bad. # Helper function to evaluate a TFLite model on all images def evaluate_model(tflite_file, model_type): test_image_indices = range(test_images.shape[0]) predictions = run_tflite_model(tflite_file, test_image_indices) accuracy = (np.sum(test_labels== predictions) * 100) / len(test_images) print('%s model accuracy is %.4f%% (Number of test samples=%d)' % ( model_type, accuracy, len(test_images))) Deploying model Converting TensorFlow Lite model to C file The following code runs xxd on the quantized model, writes the output to a file called model_quantized.cc, in the file, the model is defined as an array of bytes, and prints it to the screen. The output is very long, so we won’t reproduce it all here, but here’s a snippet that includes just the beginning and end. # Save the file as a C source file xxd -i model_integer_only_quantization.tflite > model_quantized.cc # Print the source file cat model_quantized.cc Fig 11 Deploying the C file to project We use the tensorflow_lite_cifar10 demo as a prototype, then replace the original model and do some code modification, below is the code in the modified main file. #include "board.h" #include "fsl_debug_console.h" #include "pin_mux.h" #include "timer.h" #include <iomanip> #include <iostream> #include <string> #include <vector> #include "tensorflow/lite/kernels/register.h" #include "tensorflow/lite/model.h" #include "tensorflow/lite/optional_debug_tools.h" #include "tensorflow/lite/string_util.h" #include "get_top_n.h" #include "model.h" #define LOG(x) std::cout // ---------------------------- Application ----------------------------- // Lenet Mnist model input data size (bytes). #define LENET_MNIST_INPUT_SIZE 28*28*sizeof(char) // Lenet Mnist model number of output classes. #define LENET_MNIST_OUTPUT_CLASS 10 // Allocate buffer for input data. This buffer contains the input image // pre-processed and serialized as text to include here. uint8_t imageData[LENET_MNIST_INPUT_SIZE] = { #include "clothes_select.inc" }; /* Tresholds */ #define DETECTION_TRESHOLD 60 /*! * @brief Initialize parameters for inference * * @param reference to flat buffer * @param reference to interpreter * @param pointer to storing input tensor address * @param verbose mode flag. Set true for verbose mode */ void InferenceInit(std::unique_ptr<tflite::FlatBufferModel> &model, std::unique_ptr<tflite::Interpreter> &interpreter, TfLiteTensor** input_tensor, bool isVerbose) { model = tflite::FlatBufferModel::BuildFromBuffer(Fashion_MNIST_model, Fashion_MNIST_model_len); if (!model) { LOG(FATAL) << "Failed to load model\r\n"; return; } tflite::ops::builtin::BuiltinOpResolver resolver; tflite::InterpreterBuilder(*model, resolver)(&interpreter); if (!interpreter) { LOG(FATAL) << "Failed to construct interpreter\r\n"; return; } int input = interpreter->inputs()[0]; const std::vector<int> inputs = interpreter->inputs(); const std::vector<int> outputs = interpreter->outputs(); if (interpreter->AllocateTensors() != kTfLiteOk) { LOG(FATAL) << "Failed to allocate tensors!"; return; } /* Get input dimension from the input tensor metadata assuming one input only */ *input_tensor = interpreter->tensor(input); auto data_type = (*input_tensor)->type; if (isVerbose) { const std::vector<int> inputs = interpreter->inputs(); const std::vector<int> outputs = interpreter->outputs(); LOG(INFO) << "input: " << inputs[0] << "\r\n"; LOG(INFO) << "number of inputs: " << inputs.size() << "\r\n"; LOG(INFO) << "number of outputs: " << outputs.size() << "\r\n"; LOG(INFO) << "tensors size: " << interpreter->tensors_size() << "\r\n"; LOG(INFO) << "nodes size: " << interpreter->nodes_size() << "\r\n"; LOG(INFO) << "inputs: " << interpreter->inputs().size() << "\r\n"; LOG(INFO) << "input(0) name: " << interpreter->GetInputName(0) << "\r\n"; int t_size = interpreter->tensors_size(); for (int i = 0; i < t_size; i++) { if (interpreter->tensor(i)->name) { LOG(INFO) << i << ": " << interpreter->tensor(i)->name << ", " << interpreter->tensor(i)->bytes << ", " << interpreter->tensor(i)->type << ", " << interpreter->tensor(i)->params.scale << ", " << interpreter->tensor(i)->params.zero_point << "\r\n"; } } LOG(INFO) << "\r\n"; } } /*! * @brief Runs inference input buffer and print result to console * * @param pointer to image data * @param image data length * @param pointer to labels string array * @param reference to flat buffer model * @param reference to interpreter * @param pointer to input tensor */ void RunInference(const uint8_t* image, size_t image_len, const std::string* labels, std::unique_ptr<tflite::FlatBufferModel> &model, std::unique_ptr<tflite::Interpreter> &interpreter, TfLiteTensor* input_tensor) { /* Copy image to tensor. */ memcpy(input_tensor->data.uint8, image, image_len); /* Do inference on static image in first loop. */ auto start = GetTimeInUS(); if (interpreter->Invoke() != kTfLiteOk) { LOG(FATAL) << "Failed to invoke tflite!\r\n"; return; } auto end = GetTimeInUS(); const float threshold = (float)DETECTION_TRESHOLD /100; std::vector<std::pair<float, int>> top_results; int output = interpreter->outputs()[0]; TfLiteTensor *output_tensor = interpreter->tensor(output); TfLiteIntArray* output_dims = output_tensor->dims; // assume output dims to be something like (1, 1, ... , size) auto output_size = output_dims->data[output_dims->size - 1]; /* Find best image candidates. */ GetTopN<uint8_t>(interpreter->typed_output_tensor<uint8_t>(0), output_size, 1, threshold, &top_results, false); if (!top_results.empty()) { auto result = top_results.front(); const float confidence = result.first; const int index = result.second; if (confidence * 100 > DETECTION_TRESHOLD) { LOG(INFO) << "----------------------------------------\r\n"; LOG(INFO) << " Inference time: " << (end - start) / 1000 << " ms\r\n"; LOG(INFO) << " Detected: " << std::setw(10) << labels[index] << " (" << (int)(confidence * 100) << "%)\r\n"; LOG(INFO) << "----------------------------------------\r\n\r\n"; } } } /*! * @brief Main function */ int main(void) { const std::string labels[] = {"T-shirt/top", "Trouser","Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"}; /* Init board hardware. */ BOARD_ConfigMPU(); BOARD_InitPins(); BOARD_BootClockRUN(); BOARD_InitDebugConsole(); InitTimer(); std::unique_ptr<tflite::FlatBufferModel> model; std::unique_ptr<tflite::Interpreter> interpreter; TfLiteTensor* input_tensor = 0; InferenceInit(model, interpreter, &input_tensor, false); LOG(INFO) << "Fashion MNIST object recognition example using a TensorFlow Lite model.\r\n"; LOG(INFO) << "Detection threshold: " << DETECTION_TRESHOLD << "%\r\n"; /* Run inference on static ship image. */ LOG(INFO) << "\r\nStatic data processing:\r\n"; RunInference((uint8_t*)imageData, (size_t)LENET_MNIST_INPUT_SIZE, labels, model, interpreter, input_tensor); while(1) {} } Testing result After deploying the model in the demo project, then we'll run this demo on the MIMXRT1060 (Fig 12) board for testing. Fig 12 Run the below code to covert the Fashion MNIST image to text The process_image() function can convert a Fashion MNIST image to an include file as static data, then include this file in the demo project. def process_image(image, output_path, num_batch=1): img_data = np.transpose(image, (2, 0, 1)) # Repeat image for batch processing (resulting tensor is NCHW or NHWC) img_data = np.reshape(img_data, (num_batch, img_data.shape[0], img_data.shape[1], img_data.shape[2])) img_data = np.repeat(img_data, num_batch, axis=0) img_data = np.reshape(img_data, (num_batch, img_data.shape[1], img_data.shape[2], img_data.shape[3])) # Serialize image batch img_data_bytes = bytearray(img_data.tobytes(order='C')) image_bytes_per_line = 20 with open(output_path, 'wt') as f: idx = 0 for byte in img_data_bytes: f.write('0X%02X, ' % byte) if idx % image_bytes_per_line == (image_bytes_per_line - 1): f.write('\n') idx = idx + 1 # Return serialized image size return len(img_data_bytes)      2. Run the demo project on board.
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Introduction A common need for GUI applications is to implement a clock function.  Whether it be to create a clock interface for the end user's benefit, or just to time animations or other actions, implementing an accurate clock is a useful and important feature for GUI applications.  The aim of this document is to help you implement clock functions in your AppWizard project.   Methods When implementing a real-time clock, there are a couple of general methods to do so.   Use an independent timer in your MCU Using animation objects Each of these methods have their advantages and disadvantages.  If you just need a timer that doesn't require extra code and you don't require control or assurance of precision, or maybe you can't spare another timer, using an animation object (method #2) may be a good option in that application.  If your application requires an assurance of precision or requires other real-time actions to be performed that AppWizard can't control, it is best to implement an independent timer in your MCU (method #1).  Method 1:  Independent MCU Timer Implementing a timer via an independent MCU timer allows better control and guarantees the precision because it isn't a shared clock and the developer can adjust the interrupt priorities such that the timer interrupt has the highest priority.  AppWizard timing uses a common timer and then time slices activities similar to how an operating system works.  It is for this reason that implementing an independent MCU timer is best when you need control over the precision of the timer or you need other real-time actions to be triggered by this timer.  When implementing a timer using an independent MCU timer (like the RTC module), an understanding of how to interact with Text widgets is needed. Let's look at this first.   Interacting with Text Widgets Editing Text widgets occurs through the use of the emWin library API (the emWin library is the underlying code that AppWizard builds upon). The Text widget API functions are documented in the emWin Graphic Library User Guide and Reference Manual, UM3001.  Most of the Text widget API functions require a Text widget handle.  Be sure to not confuse this handle for the AppWizard ID.  Imagine a clock example where there are two Text widgets in the interface:  one for the minutes and one for the seconds.  The AppWizard IDs of these objects might be ID_TEXT_MINS and ID_TEXT_SECONDS respectively (again, these are not to be confused with the handle to the Text widget for use by emWin library functions).  The first action software should take is to obtain the handle for the Text widgets.   This can be done using the WM_GetDialogItem function.  The code to get the active window handle and the handle for the two Text widgets is shown below: activeWin = WM_GetActiveWindow(); textBoxMins = WM_GetDialogItem(activeWin, ID_TEXT_MINS); textBoxSecs = WM_GetDialogItem(activeWin, ID_TEXT_SECONDS);‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍ Note that this function requires the handle to the parent window of the Text widget.  If your application has multiple windows or screens, you may need to be creative in how you acquire this handle, but for this example, the software can simply call the WM_GetActiveWindow function (since there is only one screen).  When to call these functions can be a bit tricky as well.  They can be called before the MainTask() function of the application is called and the application will not crash.  However, the handles won't be correct and the Text widgets will not be updated as expected.  It's recommended that these handles be initialized when the screen is initialized.  An example of how this would be done is shown below: void cbID_SCREEN_CLOCK(WM_MESSAGE * pMsg) { extern WM_HWIN activeWin; extern WM_HWIN textBoxMins; extern WM_HWIN textBoxSecs; extern WM_HWIN textBoxDbg; if(pMsg->MsgId == WM_INIT_DIALOG) { activeWin = WM_GetActiveWindow(); textBoxMins = WM_GetDialogItem(activeWin, ID_TEXT_MINS); textBoxSecs = WM_GetDialogItem(activeWin, ID_TEXT_SECONDS); textBoxDbg = WM_GetDialogItem(activeWin, ID_TEXT_DBG); } GUI_USE_PARA(pMsg); }‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍ Once the Text widget handles have been acquired, the text can be updated using the TEXT_SetText() function or the TEXT_SetDec() function in this case, because the Text widgets are configured for decimal mode, since we want to display numbers.  An example of the code to do this is shown below.  /* TEXT_SetDec(Text Widget Handle, Value as Int, Length, Shift, Sign, Leading Spaces) */ if(TEXT_SetDec(textBoxSecs, (int)gSecs, 2, 0, 0, 0)) { /* Perform action here if necessary */ } if(TEXT_SetDec(textBoxMins, (int)gMins, 2, 0, 0, 0)) { /* Perform action here if necessary */ } ‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍ Method 2:  Animation Objects When implementing a real-time clock using animation objects, it is necessary to implement a loop.  This could be done outside of the AppWizard GUI (in your code) but because the timing precision can't be guaranteed, it's just as easy to implement a loop in the AppWizard GUI if you know how (it isn't very intuitive as to how to do this). Before examining the interactions to do this, let's look at the variables and objects needed to do this.  ID_VAR_SECS - This variable holds the current seconds value. ID_VAR_SECS_1 - This variable holds the next second value.  ID_TEXT_SECONDS - Text box that displays the current seconds value. ID_END_CNT - Variable that holds the value at which the seconds rolls over and increments the minute count ID_TEXT_MINS - Text box that holds the current minute count. ID_MIN_END_CNT - Variable that holds the value at which the minutes rolls over (which would also increment the hour count if the hours were implemented). ID_BUTTON_SECS - This is a hidden button that initiates actions when the seconds variable has reached the end count.  Now, here are the interactions used to implement the clock feature using animation interactions.  The heart of the loop are the interactions triggered by ID_VAR_SECS.  ID_VAR_SECS -> ID_VAR_SECS_1:  When ID_VAR_SECS changes, it needs to add one to ID_VAR_SECS_1 so that the animation will animate to one second from the current time. ID_VAR_SECS -> ID_TEXT_SECONDS:  When ID_VAR_SECS changes, it also needs to start the animation from the current value to the next second (ID_VAR_SECS_1). A very essential part of the loop is ensuring the animation restarts every time.  So ID_TEXT_SECONDS needs to change the value of ID_VAR_SECS when the animation ends. ID_VAR_SECS is changed to the current time value, ID_VAR_SECS_1. When the ID_TEXT_SECONDS animation ends, it must also decrement the ID_VAR_END_CNT variable.  This is analogous to the control variable of a "For" loop being updated. This is done using the ADDVALUE job, adding '-1' to the variable, ID_VAR_END_CNT. When ID_VAR_END_CNT changes, it updates the hidden button, ID_BUTTON_SECS, with the new value.  This is analogous to a "For" loop checking whether its control variable is still within its limits.   The interactions in group 5 are interactions that restart the loop when the seconds reach the count that we desire.  When the loop is restarted, the following actions must be taken: Set ID_VAR_SECS and ID_VAR_SECS_1 to the initial value for the next loop ('0' in this case).  Note that ID_VAR_SECS_1 MUST be set before ID_VAR_SECS.  Additionally, if the loop is to continue, ID_VAR_SECS and ID_VAR_SECS_1 must be set to the same value.   ID_TEXT_SECONDS is set to the initial value.  If this isn't done, then the text box will try to animate from the final value to the initial value and then will look "weird". ID_VAR_END_CNT is reset to its initial value (60 in this case).  ID_BUTTON_SECS is also responsible for updating the minutes values.  In this case, it's incrementing the ID_TEXT_MINS value (counting up in minutes) and decrementing the ID_VAR_MIN_END_CNT  Adjusting the time of an animation object The animation object (as well as other emWin objects) use the GUI_X_DELAY function for timing.  It is up to the host software to implement this function.  In the i.MX RT examples, the General Purpose Timer (GPT) is used for this timer.  So how the GPT is configured will affect the timing of the application and the how fast or slow the animations run. The GPT is configured in the function BOARD_InitGPT() which resides in the main source file.  The recommended way to adjust the speed of the timer is by changing the divider value to the GPT. Conclusion So we have seen two different methods of implementing a real-time clock in an AppWizard GUI application.  Those methods are: Use an independent timer in your MCU Using animation objects Using an independent timer in your MCU may be preferred as it allows for better control over the timing, can allow for real-time actions to be performed that AppWizard can't control, and provides some assurance of precision.  Using animation objects may be preferred if you just need a quick timer implementation that doesn't require you to manually add code to your project or use a second timer.  
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Overview of i.MX RT1050         The i.MX RT1050 is the industry's first crossover processor and combines the high-performance and high level of integration on an applications processors with the ease of use and real-time functionality of a micro-controller. The i.MX RT1050 runs on the Arm Cortex-M7 core at 600 MHz, it means that it definitely has the ability to do some complicated computing, such as floating-point arithmetic, matrix operation, etc. For general MCU, they're hard to conquer these complicated operations.         It has a rich peripheral which makes it suit for a variety of applications, in this demo, the PXP (Pixel Pipeline), CSI (CMOS Sensor Interface), eLCDIF (Enhanced LCD Interface) allows me to build up camera display system easily Fig 1 i.MX RT series           It has a rich peripheral which makes it suit for a variety of applications, in this demo, the PXP (Pixel Pipeline), CSI (CMOS Sensor Interface), eLCDIF (Enhanced LCD Interface) allows me to build up camera display system easily Fig 2 i.MX RT1050 Block Diagram Basic concept of Compute Vision (CV)          Machine Learning (ML) is moving to the edge because of a variety of reasons, such as bandwidth constraint, latency, reliability, security, ect. People want to have edge computing capability on embedded devices to provide more advanced services, like voice recognition for smart speakers and face detection for surveillance cameras. Fig 3 Reason        Convolutional Neural Networks (CNNs) is one of the main ways to do image recognition and image classification. CNNs use a variation of multilayer perception that requires minimal pre-processing, based on their shared-weights architecture and translation invariance characteristics. Fig 4 Structure of a typical deep neural network         Above is an example that shows the original image input on the left-hand side and how it progresses through each layer to calculate the probability on the right-hand side. Hardware MIMXRT1050 EVK Board; RK043FN02H-CT(LCD Panel) Fig 5 MIMXRT1050 EVK board Reference demo code emwin_temperature_control: demonstrates graphical widgets of the emWin library. cmsis_nn_cifar10: demonstrates a convolutional neural network (CNN) example with the use of convolution, ReLU activation, pooling and fully-connected functions from the CMSIS-NN software library. The CNN used in this example is based on the CIFAR-10 example from Caffe. The neural network consists of 3 convolution layers interspersed by ReLU activation and max-pooling layers, followed by a fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which is classified into one of the 10 output classes. Note: Both of these two demo projects are from the SDK library Deploy the neuro network mode Fig 6 illustrates the steps of deploying the neuro network mode on the embedded platform. In the cmsis_nn_cifar10 demo project, it has provided the quantized parameters for the 3 convolution layer, so in this implementation, I use these parameters directly, BTW, I choose 100 images randomly from the Test set as a round of input to evaluate the accuracy of this model. And through several rounds of testing, I get the model's accuracy is about 65% as the below figure shows. Fig 6 Deploy the neuro network mode Fig 7 cmsis_nn_cifar10 demo project test result The CIFAR-10 dataset is a collection of images that are commonly used to train ML and computer vision algorithms, it consists of 60000 32x32 color images in 10 classes, with 6000 images per class ("airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"). There are 50000 training images and 10000 test images. Embedded platform software structure         After POR, various components are initialized, like system clock, pin mux, camera, CSI, PXP, LCD and emWin, etc. Then control GUI will show up in the LCD, press the Play button will display the camera video in the LCD, once an object into the camera's window, you can press the Capture button to pause the display and run the model to identify the object. Fig8 presents the software structure of this demo. Fig 8 Embedded platform software structure Object identify Test The three figures present the testing result.   Fig 9 Fig 10 Fig 11 Furture work          Use the Pytorch framework to train a better and more complicated convolutional network for object recognition usage.
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Source code: https://github.com/JayHeng/NXP-MCUBootUtility   【v2.0.0】 Features: > 1. Support i.MXRT5xx A0, i.MXRT6xx A0 >    支持i.MXRT5xx A0, i.MXRT6xx A0 > 2. Support i.MXRT1011, i.MXRT117x A0 >    支持i.MXRT1011, i.MXRT117x A0 > 3. [RTyyyy] Support OTFAD encryption secure boot case (SNVS Key, User Key) >     [RTyyyy] 支持基于OTFAD实现的安全加密启动(唯一SNVS key,用户自定义key) > 4. [RTxxx] Support both UART and USB-HID ISP modes >     [RTxxx] 支持UART和USB-HID两种串行编程方式(COM端口/USB设备自动识别) > 5. [RTxxx] Support for converting bare image into bootable image >     [RTxxx] 支持将裸源image文件自动转换成i.MXRT能启动的Bootable image > 6. [RTxxx] Original image can be a bootable image (with FDCB) >     [RTxxx] 用户输入的源程序文件可以包含i.MXRT启动头 (FDCB) > 7. [RTxxx] Support for loading bootable image into FlexSPI/QuadSPI NOR boot device >     [RTxxx] 支持下载Bootable image进主动启动设备 - FlexSPI/QuadSPI NOR接口Flash > 8. [RTxxx] Support development boot case (Unsigned, CRC) >     [RTxxx] 支持用于开发阶段的非安全加密启动(未签名,CRC校验) > 9. Add Execute action support for Flash Programmer >     在通用Flash编程器模式下增加执行(跳转)操作 > 10. [RTyyyy] Can show FlexRAM info in device status >       [RTyyyy] 支持在device status里显示当前FlexRAM配置情况 Improvements: > 1. [RTyyyy] Improve stability of USB connection of i.MXRT105x board >     [RTyyyy] 提高i.MXRT105x目标板USB连接稳定性 > 2. Can write/read RAM via Flash Programmer >    通用Flash编程器里也支持读写RAM > 3. [RTyyyy] Provide Flashloader resident option to adapt to different FlexRAM configurations >     [RTyyyy] 提供Flashloader执行空间选项以适应不同的FlexRAM配置 Bugfixes: > 1. [RTyyyy] Sometimes tool will report error "xx.bat file cannot be found" >     [RTyyyy] 有时候生成证书时会提示bat文件无法找到,导致证书无法生成 > 2. [RTyyyy] Editing mixed eFuse fields is not working as expected >     [RTyyyy] 可视化方式去编辑混合eFuse区域并没有生效 > 3. [RTyyyy] Cannot support 32MB or larger LPSPI NOR/EEPROM device >     [RTyyyy] 无法支持32MB及以上容量的LPSPI NOR/EEPROM设备 > 4. Cannot erase/read the last two pages of boot device via Flash Programmer >    在通用Flash编程器模式下无法擦除/读取外部启动设备的最后两个Page
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[中文翻译版] 见附件 原文链接: https://community.nxp.com/docs/DOC-342297
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Introduction  This document is an extension of section 3.1.3, “Software implementation” from the application note AN12077, using the i.MX RT FlexRAM. It's important that before continue reading this document, you read this application note carefully.  Link to the application note.  Section 3.1.3 of the application note explains how to reallocate the FlexRAM through software within the startup code of your application. This document will go into further detail on all the implications of making these modifications and what is the best way to do it.  Prerequisites RT10xx-EVK  The latest SDK which you can download from the following link: Welcome | MCUXpresso SDK Builder MCUXpresso IDE Internal SRAM  The amount of internal SRAM varies depending on the RT. In some cases, not all the internal SRAM can be reallocated with the FlexRAM.  RT  Internal SRAM FlexRAM RT1010 Up to 128 KB Up to 128 KB RT1015 Up to 128 KB Up to 128 KB RT1020 Up to 256 KB Up to 256 KB RT1050 Up to 512 KB Up to 512 KB RT1060 Up to 1MB  Up to 512 KB RT1064 Up to 1MB Up to 512 KB   In the case of the RT106x, only 512 KB out of the 1MB of internal SRAM can be reallocated through the FlexRAM as DTCM, ITCM, and OCRAM. The remaining 512 KB are from OCRAM and cannot be reallocated. For all the other RT10xx you can reallocate the whole internal SRAM either as DTCM, ITCM, and OCRAM. Section 3.1.3.1 of the application note explains the limitations of the size when reallocating the FlexRAM. One thing that's important to mention is that the ROM bootloader in all the RT10xx parts uses the OCRAM, hence you should keep some  OCRAM when reallocating the FlexRAM, this doesn't apply to the RT106x since you will always have the 512 KB of OCRAM that cannot be reallocated. To know more about how many OCRAM each RT family needs please refer to section 2.1.1.1 of the application note. Implementation in MCUXpresso IDE First, you need to import any of the SDK examples into your MCUXpresso IDE workspace. In my case, I imported the igpio_led_output example for the RT1050-EVKB. If you compile this project, you will see that the default configuration for the FlexRAM on the RT1050-EVKB is the following:  SRAM_DTC 128 KB SRAM_ITC 128 KB SRAM_OC 256 KB   Now we need to go to the Reset handler located in the file startup_mimxrt1052.c. Reallocating the FlexRAM has to be done before the FlexRAM is configured, this is why it's done inside the Reset Handler.  The registers that we need to modify to reallocate the FlexRAM are IOMUXC_GPR_GPR16, and IOMUXC_GPR_GPR17. So first we need to have in hand the addresses of these three registers. Register Address IOMUXC_GPR_GPR16 0x400AC040 IOMUXC_GPR_GPR17 0x400AC044   Now, we need to determine how we want to reallocate the FlexRAM to see the value that we need to load into register IOMUXC_GPR_GPR17. In my case, I want to have the following configuration:  SRAM_DTC 256 KB SRAM_ITC 128 KB SRAM_OC 128 KB   When choosing the new sizes of the FlexRAM be sure that you choose a configuration that you can also apply through the FlexRAM fuses, I will explain the reason for this later. The configurations that you can achieve through the fuses are shown in the Fusemap chapter of the reference manual in the table named "Fusemap Descriptions", the fuse name is "Default_FlexRAM_Part".  Based on the following explanation of the IOMUXC_GPR_GPR17 register: The value that I need to load to the register is 0xAAAAFF55. Where the first  4 banks correspond to the 128KB of SRAM_OC, the next 4 banks correspond to the 128KB of SRAM_ITC and the last 8 banks are the 256KB of SRAM_DTC.  Now, that we have all the addresses and the values that we need we can start writing the code in the Reset handler. The first thing to do is load the new value into the register IOMUXC_GPR_GPR17. After, we need to configure register IOMUXC_GPR_GPR16 to specify that the FlexRAM bank configuration should be taken from register IOMUXC_GPR_GPR17 instead of the fuses. Then if in your new configuration of the FlexRAM either the SRAM_DTC or SRAM_ITC are of size 0, you need to disable these memories in the register IOMUXC_GPR_GPR16. At the end your code should look like the following:    void ResetISR(void) { // Disable interrupts __asm volatile ("cpsid i"); /* Reallocating the FlexRAM */ __asm (".syntax unified\n" "LDR R0, =0x400ac044\n"//Address of register IOMUXC_GPR_GPR17 "LDR R1, =0xaaaaff55\n"//FlexRAM configuration DTC = 265KB, ITC = 128KB, OC = 128KB "STR R1,[R0]\n" "LDR R0,=0x400ac040\n"//Address of register IOMUXC_GPR_GPR16 "LDR R1,[R0]\n" "ORR R1,R1,#4\n"//The 4 corresponds to setting the FLEXRAM_BANK_CFG_SEL bit in register IOMUXC_GPR_GPR16 "STR R1,[R0]\n" #ifdef FLEXRAM_ITCM_ZERO_SIZE "LDR R0,=0x400ac040\n"//Address of register IOMUXC_GPR_GPR16 "LDR R1,[R0]\n" "AND R1,R1,#0xfffffffe\n"//Disabling SRAM_ITC in register IOMUXC_GPR_GPR16 "STR R1,[R0]\n" #endif #ifdef FLEXRAM_DTCM_ZERO_SIZE "LDR R0,=0x400ac040\n"//Address of register IOMUXC_GPR_GPR16 "LDR R1,[R0]\n" "AND R1,R1,#0xfffffffd\n"//Disabling SRAM_DTC in register IOMUXC_GPR_GPR16 "STR R1,[R0]\n" #endif ".syntax divided\n"); #if defined (__USE_CMSIS) // If __USE_CMSIS defined, then call CMSIS SystemInit code SystemInit(); ...‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍   If you compile your project you will see the memory distribution that appears on the console is still the default configuration.  This is because we did modify the Reset handler to reallocate the FlexRAM but we haven't modified the linker file to match these new sizes. To do this you need to go to the properties of your project. Once in the properties, you need to go to C/C++ Build -> MCU settings. Once you are in the MCU settings you need to modify the sizes of the SRAM memories to match the new configuration.  When you make these changes click Apply and Close. After making these changes if you compile the project you will see the memory distribution that appears in the console is now matching the new sizes.  Now we need to modify the Memory Protection Unit (MPU) to match these new sizes of the memories. To do this you need to go to the function BOARD_ConfigMPU inside the file board.c. Inside this function, you need to locate regions 5, 6, and 7 which correspond to SRAM_ITC, SRAM_DTC, and SRAM_OC respectively. Same as for register IOMUXC_GPR_GPR14, if the new size of your memory is not 32, 64, 128, 256, or 512 you need to choose the next greater number. Your configuration should look like the following:    /* Region 5 setting: Memory with Normal type, not shareable, outer/inner write back */ MPU->RBAR = ARM_MPU_RBAR(5, 0x00000000U); MPU->RASR = ARM_MPU_RASR(0, ARM_MPU_AP_FULL, 0, 0, 1, 1, 0, ARM_MPU_REGION_SIZE_128KB); /* Region 6 setting: Memory with Normal type, not shareable, outer/inner write back */ MPU->RBAR = ARM_MPU_RBAR(6, 0x20000000U); MPU->RASR = ARM_MPU_RASR(0, ARM_MPU_AP_FULL, 0, 0, 1, 1, 0, ARM_MPU_REGION_SIZE_256KB); /* Region 7 setting: Memory with Normal type, not shareable, outer/inner write back */ MPU->RBAR = ARM_MPU_RBAR(7, 0x20200000U); MPU->RASR = ARM_MPU_RASR(0, ARM_MPU_AP_FULL, 0, 0, 1, 1, 0, ARM_MPU_REGION_SIZE_128KB);‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍   We need to change the image entry address to the Reset handler. To do this, you need to go to the file fsl_flexspi_nor_boot.c inside the xip folder. You need to declare the ResetISR and change the entry address in the image vector table.  Finally, we need to place the stack at the start of the DTCM memory. To do this, we need to go to the properties of your project. From there, we have to C/C++ Build and Manage Linker Script.  From there, we will need to add two more assembly instructions in our ResetISR function. We have to add these two instructions at the beginning of our assembly code:  In the attached c file, you'll find all the assembly instructions mentioned above.  That's it, these are all the changes that you need to make to reallocate the FlexRAM during the startup.  Debug Session  To verify that all the modifications that we just did were correct we will launch the debug session. As soon as we reach the main, before running the application, we will go to the peripheral view to see registers IOMUXC_GPR_GPR16, and IOMUXC_GPR_GPR17 and verify that the values are the correct ones. In register IOMUXC_GPR_GPR16 as shown in the image below we configure the FLEXRAM_BANK_CFG_SEL as 1 to use the use register IOMUXC_GPR_GPR17 to configure the FlexRAM.  Finally, in register IOMUXC_GPR_GPR17 we can see the value 0xAAAAFF55 that corresponds to the new configuration.  Reallocating the FlexRAM through the Fuses  We just saw how to reallocate the FlexRAM through software by writing some code in the Reset Handler. This procedure works fine, however, it's recommended that you use this approach to test the different sizes that you can configure but once you find the correct configuration for your application we highly recommend that you configure these new sizes through the fuses instead of using the register IOMUXC_GPR_GPR17. There are lots of dangerous areas in reconfiguring the FlexRAM in code. It pretty much all boils down to the fact that any code/data/stack information written to the RAM can end up changing location during the reallocation.  This is the reason why once you find the correct configuration, you should apply it through the fuses. If you use the fuses to configure the FlexRAM, then you don't have the same concerns about moving around code and data, as the fuse settings are applied as a hardware default.  Keep in mind that once you burn the fuses there's no way back! This is why it's important that you first try the configuration through the software method. Once you burn the fuses you won't need to modify the Reset handler, you only need to modify the MPU to change the size of regions as we saw before and the MCU settings of your project to match the new memory sizes that you configured through the fuses.  The fuse in charge of the FlexRAM configuration is Default_FlexRAM_Part, the address of this fuse is 0x6D0[15:13]. You can find more information about this fuse and the different configurations in the Fusemap chapter of the reference manual.  To burn the fuses I recommend using either the blhost or the MCUBootUtility.  Link to download the blhost.  Link to the MCUBootUtility webpage.    I hope you find this document helpful!  Víctor Jiménez 
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[中文翻译版] 见附件   原文链接: https://community.nxp.com/community/imx/blog/2019/04/17/do-you-have-a-minute 
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