These lab guides provide step-by-step instructions on how to take a quantized TensorFlow Lite model and use the Neutron Conversion Tool found in eIQ Neutron SDK to convert the model to run on the eIQ Neutron NPU found on NXP MCU devices.  The eIQ Neutron NPU for MCX N Lab Guide  documents focus on using the Neutron Converter tool found inside eIQ Neutron SDK to convert a model and then import that converted model into an eIQ MCUXpresso SDK example. There are labs for VSCode, GCC, and MCUXpresso IDE. The labs designed to run on the FRDM-MCXN947 but the same concepts can be applied to other MCX N boards as well. There is a similar NPU lab available for i.MX RT700 too.  Also be sure to also check out the Getting Started community post for more details on the eIQ Neutron NPU. You can also explore the TFLM Getting Started Guide for information on how to use your own model and data for inference.  --- Updated June 2026 for Neutron SDK 3.1.2

The MCX N microcontroller family includes an eIQ Neutron N1-16 NPU for accelerating neural network models. The FRDM-MCXN947 development board can be combined with a camera and LCD screen to showcase running TinyML vision models on a microcontroller.   MCX N Camera Hardware Setup: The following hardware is used: MCX N FRDM Development Board - FRDM-MCXN947 OV7670 camera (with optional wide-angle lens) NXP LCD-PAR-S035  There are three small modifications needed for the FRDM-MCXN947 board for camera support. Without this modification the camera colors will be incorrect and tinted red.   Change SJ16, SJ26, and SJ27 found on the back of the Rev B board to connect pin 3 (the dashed side) so that it looks like the following:          Then connect the camera and LCD to the FRDM-MCXN947: Plug in the OV7670 camera into J11. It should line up with the orange box.                                Connect the LCD-PAR-S035 LCD into J12. It should be flush with the bottom so that the top 2 rows of pins are left hanging off the edge. Also note that on some LCD-PAR-S035 boards those top two rows of pins are not installed.            It should look like the following when complete             Also as the camera and Ethernet pins are shared, if you need to use the Ethernet+Camera at the same time please see this NXP Community post. MCX N Vision ML Examples: The NXP Application Code Hub contains several vision AI/ML examples: Face Detect Face Detect with Zephyr Multiple Person Detection  CIFAR10 Fashion MNIST There are also Multimedia Processing Pipeline (MPP) examples inside the MCX N MCUXpresso SDK that demonstrate more examples of using vision AI/ML on MCX N. These examples are only available for VSCode/GCC in the Repository-Layout SDK package. Note: It is recommended to use MCUXpresso SDK 25.09 for these examples. The MPP issues in the 25.12 and 26.03 MCUXpresso SDK releases should be fixed in the upcoming MCUXpresso SDK 26.06.    MCX N ML Vision Lab: The attached eIQ Neutron NPU for MCX N Lab Guide - Face Detect.pdf lab document walks through the steps to download an example Face Detect ML project from the NXP Application Code Hub and use the eIQ Neutron Converter tool to convert a model. It also describes how to update the eIQ and Neutron software libraries in an older MCUXpresso SDK project to work with the latest eIQ Neutron SDK libraries. It is recommended to go through the general MCX N NPU Lab Guide first and then do the attached Face Detect lab second.  The lab is also included below: 1  Lab Overview This document will demonstrate the acceleration provided by the eIQ Neutron NPU using the Multiple Face Detection demo for the FRDM-MCXN947 found on the NXP App Code Hub. The demo will run with the non-NPU optimized model and then the performance can be compared to the NPU optimized version of that same model. It also demonstrates how the NPU optimized version of the face detect model was generated. This lab is written for MCUXPresso IDE but the same basic steps can be used for VSCode or GCC.   This lab will also cover how to update the Neutron NPU libraries in the project, as the original Face Detect example uses an older Neutron library version.   It is highly recommended to complete the eIQ Neutron NPU for MCX N Lab Guide before starting this lab. 2  Software and Hardware Installation This section will cover the hardware and software needed for this lab. 2.1 Hardware The following hardware is required for this lab: MCX N FRDM Development Board - FRDM-MCXN947 OV7670 camera (with optional wide-angle lens) NXP LCD-PAR-S035 2.2 NXP Software Installation          Install MCUXpresso IDE v25.6 or later. Download the latest eIQ Neutron SDK Download and unzip the latest MCUXpresso SDK for FRDM-MCXN947 using MCUXpresso SDK builder Search for the FRDM-MCXN947 board     Then click on Others   On the SDK builder page, make sure to select the “eIQ” middleware and that the MCUXpresso IDE toolchain is selected. Then click on Build SDK.     Then click on the Download button and accept the license agreement to download the zip file.     3   Face Detection Example 3.1 Download Face Detect Demo from App Code Hub The code for this lab can be found on the NXP Application Code Hub hosted on Github, and we can use MCUXpresso IDE to directly import the Face Detection example from App Code Hub.   Drag-and-drop the FRDM-MCXN947 SDK zip file into the Installed SDKs window, located on a tab at the bottom of the screen named “Installed SDKs”. You will get the following pop-up, so hit OK.   Once imported, the Installed SDK tab will look something like this:    In the Quickstart Panel found in the lower left corner, click on Import from Application Code Hub..   In the dialog box that pops up there are many filters available to filter for different devices and types of demos. But since the name of the demo we are interested in is already known, the search box will be faster. Select the AI/ML category and then type in “face detection” and then click on the “Multiple face detection on mcxn947” demo. Make sure you don’t accidently click on the “Multiple Person Detection” demo.   On the popup that comes up, click on GitHub link at the top. At that point the Next button at the bottom will become clickable so click on that.     The next screen displays the possible branches. In this case there is only main so just click on the Next button at the bottom to go with the default.   The next dialog box determines the location on your computer where the code will be downloaded to. You can leave it at the default location if desired or click on Browse to pick your own location. Then click on Next.   The next screen will download the code and ask about importing the project. Click on Next to go with the default Import existing Eclipse projects option.   Then finally on the last screen click on Finish to import the project into your MCUXPresso IDE workspace.   You may get the following warning due to the project being made on an older version of the SDK. Then hit OK to accept the using the newest version.  15. It should look like the following when done:     3.2 Convert Model The demo is already using a model that was converted to take advantage of the eIQ Neutron NPU. This purpose of this section of the lab is to teach new NXP users how that model was converted. Unzip the eIQ Neutron SDK package in a directory of your choosing.   Optionally add <unzip_location>\eIQ_NeutronSDK_<version>\bin to your executable path so that the neutron-converter utility can be directly called from the command line. Back in MCUXpresso IDE, find the location of the original non-converted model used for this demo by right clicking on the face_detect.tflite file in source/model/ and going to Utilities->Open directory browser here.   Copy the directory location as it will be used in the next step   Open a Windows Command prompt and navigate to the directory where the model was at from the previous step               Use the Neutron Converter to convert the Face Detection model: neutron-converter --input face_detect.tflite --output face_npu.tflite --target mcxn94x     3.3 Update eIQ Neutron Libraries The Face Detect ACH example uses an older version of the eIQ Neutron libraries, and so it needs to be updated to match the Neutron libraries in newest eIQ Neutron SDK since the model was converted with that version of the Neutron Converter tool.   In the frdmmcxn947_multi_face_detection project, right click on the eiq folder and go to Utilities->Open directory browser here   Overwrite the Neutron files from the eIQ Neutron SDK folder into your project to update the Neutron libraries to the latest version: File Name Source Directory in eIQ Neutron SDK Target Directory in MCUXpresso SDK libNeutronDriver.a target\mcxn94x\board\ eiq\neutron\mcxn\cm33 libNeutronFirmware.a target\mcxn94x\board\ eiq\neutron\mcxn\cm33 NeutronDriver.h target\mcxn94x\driver\include\ eiq\neutron\driver\include NeutronErrors.h target\mcxn94x\common\include\ eiq\neutron\common\include After the new Neutron libraries are copied over, clean the project to ensure the new libraries will be used     3.4 Board modifcations There are some hardware modifications to the MCX FRDM board required for this demo since the camera pins are muxed with the Ethernet pins and the Ethernet functionality is the default.   The board version can be determined by scanning the QR code on the back of the MCX FRDM board with your phone. Most people will have Rev B boards.   Rev A: Remove the R157, R158, and R159 resistors from the back of the Rev A board so that it looks like the following:   Rev B: Change SJ16, SJ26, and SJ27 found on the back of the Rev B board to connect pin 3 (the dashed side) so that it looks like the following:       3.5 Connect the camera and LCD Plug in the OV7670 camera into J11. It should line up with the orange box.   Connect the LCD-PAR-S035 LCD into J12. Note that some older LCD-PAR-S035 LCDs may have an extra set of pins soldered on, and in that case the extra 2 rows of pins should be hanging off the edge like in the photo below.               It should look like the following when complete     3.6 Run Models Now open up model_data.s by double clicking on it, and then modify line 43 to point to the original (non NPU converted) model file named face_detect.tflite. This particular project uses the .tflite file directly.   Build the project by clicking on the Build icon in the Quickstart Panel   Then download and run the project by clicking on the Debug icon in the Quickstart Panel     You should see the demo working with an inference time of 817ms printed on the LCD display. Note: The default camera on the OV7670 is not very wide angle so you have to hold it fairly far back. There are wide-angle lenses that can be purchased to make it easier to demonstrate. Note: After POR there may be some glitching on the camera due to the fact the camera is expecting 2.8V but the board is at 3.3V and the initial HSYNC signal was missed. Press the reset button (SW1) and it should fix any camera issue.  Now let’s use the Neutron optimized model by opening model_data.s again and this time selecting the NPU converted model face_npu.tflite   Recompile and reprogram the board. You’ll see it is significantly faster with a 22ms inference time, a 37x improvement!   4  Conclusion This lab demonstrated how the eIQ Neutron NPU on MCX N devices can significantly decrease inference time on quantized models and the steps to generate a NPU optimized model using the command line tools. Also explore the other App Code Hub ML examples available online.

Part 1: Introduction The eIQ Neutron Neural Processing Unit (NPU) is a highly scalable accelerator core architecture that provides machine learning (ML) acceleration. Compared to traditional MCUs like the Kinetis series and LPC series, the MCX N series marks the first integration of NXP's eIQ® Neutron NPU for ML acceleration. The eIQ Neutron NPU offers up to 38 times faster machine learning inference performance compared to a standalone CPU core. Specifically, the MCX N94 can execute 4.8G (150MHz * 4 * 4 * 2) INT8 operations per second.   Hardware Environment: Development Board: FRDM-MCXN947 Display: 3.5" TFT LCD (PAR-LCD-S035) Camera: OV7670 Instructions for putting together demo   Software Environment: eIQ Neutron SDK MCUXpresso IDE Label CIFAR10 Image Demo on NXP App Code Hub   Part 2: Basic Model Classification Training and Deployment The main content is divided into three steps: model training, model converting, and model deployment. 1. Dataset Preparation A fruit dataset is prepared for a simple demonstration of binary classification between apples and bananas. The training set and test set are split in an 8:2 ratio.  2. Use a model generation tool like eIQ Model Creator or TensorFlow to train a model based on the image dataset.  3. Convert to TensorFlow Lite for Neutron (.tflite) Unzip the eIQ Neutron SDK and use the neutron-converter command line tool to convert the trained TensorFlow Lite model to an eIQ Neutron enabled TFLite model: neutron-converter --target mcxn94x --input fruit_model.tflite --output fruit_model_npu.tflite    4. Deploy the Model to the Label CIFAR10 Image Project This example is based on a machine learning algorithm supported by the MCXN947, which can label images captured from a camera and display the type of object at the bottom of the LCD. The model is trained on the CIFAR10 dataset, which supports 10 categories of images: "Airplane", "Automobile", "Bird", "Cat", "Deer", "Dog", "Frog", "Horse", "Ship", "Truck". a. Open MCUXpresso IDE and import the Label CIFAR10 Image project from the Application Code Hub, as follows:   b. Select the project, click on "GitHub link" -> "Next", as shown below:   c. Set the save path, click "Next" -> "Next" -> "Finish", as shown below:   d. After successful import, click on the "source" folder -> "model" folder, open "model_data.s", and copy the model file converted using eIQ Neutron SDK into the "model" folder. Modify the name of the imported model (the name of the converted model) in "model_data.s", as shown below:     e. Click on the "source" folder -> "model" folder -> open the "labels.h" file. Modify the "labels[]" array to match the order of labels model output as shown below:   f. The eIQ middleware in the project needs to be updated to be compatible with the version of the eIQ Neutron SDK used to convert the TFLite model. Update the eIQ middleware folder in the project, including the eIQ Neutron libraries, as described in this Community post or in this lab.   f. Compile the project and download it to the development board.   Part 3: Results     Part 4: Summary By efficiently utilizing the powerful performance of the eIQ Neutron NPU and the eIQ enablement tools like eIQ Neutron SDK, developers can significantly streamline the entire process from model training to deployment. This not only accelerates the development cycle of machine learning applications but also enhances their performance and reliability. Therefore, for developers looking to implement efficient machine learning applications on MCX N-series edge devices, mastering these technologies and tools is crucial.   We can also refer to the video for detailed steps using the deprecated eIQ Toolkit to generate a model. https://www.bilibili.com/video/BV1SS411N7Hv?t=12.9  For more vision examples on MCX N see this Community Post.

eIQ Time Series Studio includes a command line interface (CLI) that allows you to generate time series models via the command line just like you would in the GUI. Full documentation of this feature can be found in the eIQ TSS documentation A Quick Start with the basic key commands is also available.  In TSS version 1.5.5, there are a few command line interface issues to be aware of that will be addressed in the next TSS release. In the meantime, here are workarounds and clarifications: Library Compile Error Generating the TSS library with the library compile option may not work in all circumstances and may return a "Not Found" error.  Workaround: This will be fixed in the next TSS release.    Windows Workspace Permissions On Windows PCs, specify the workspace location when launching the TSS CLI server. The default workspace on Windows may not work properly due to a permissions issue, resulting in the following error: [PYI-12556:ERROR] Failed to execute script 'server' due to unhandled exception!   Workaround: Specify a workspace directory location where TSS has read/write access with the tss_cli engine launch --workspace <directory_location> argument   Do Not Run CLI and GUI Simultaneously The TSS CLI and TSS GUI should not be run at the same time. Also only one user should interact with the TSS CLI at a time to avoid race conditions in the TSS database if it is on a shared server.    Engine Launch Syntax Correction The TSS CLI documentation uses tss_cli engine launch –engine <path to tss_engine>, but the correct syntax is: tss_cli engine –exe_path <path to tss_engine>   Windows Command Prompt Delimiters When using Windows Command Prompt and importing sensor data with the signal import command, use double quotes (" ") to specify the delimiters (ie a space in this case) instead of the single quotes ('  ')    Terminology Clarification "Optimization" in the documentation refers to the process of training the time series model.   Finding the Optimization ID during training The my-opt-id value can be found by viewing the optimization list with tss_cli optimization list --project_name cli_test_project   Finding the Label Name The label-name value used for emulation commands can be found by viewing the name of the signals with tss_cli signal list --project_name cli_test_project   Boolean Arguments Arguments such as quick search during training are Boolean values, which are automatically enabled when included as part of the command line argument. For example, use -qs rather than -qs true. Below is an example using the command line interface with eIQ Time Series Studio: #Assumes the following items: #1) tss_cli is in the executable path (C:\Program Files\NXP\eIQ_TimeSeriesStudio-1.5.5) #2) The dataset is in C:\tss\dataset #3) A workspace will be setup in C:\tss\workspace #4) The license key has already been retrieved from the TSS GUI #5) The TSS GUI is not also concurrently running #Install license Key tss_cli.exe license activate --key <your_key> #Start TSS CLI tss_cli engine launch -e "C:\Program Files\NXP\eIQ_TimeSeriesStudio-1.5.5\tss_engine\tss_engine.exe" --port 18000 --workspace "C:\tss\workspace" #Install license key tss_cli license activate --key <your_key> #Create a classification project for a FRDM-MCXN947 tss_cli project create --project_name cli_fan_project --algo_type cls --device FRDM-MCXN947 --channels 3 --label_target_num 4 #List all projects in the workspace and see details for the newly generated cli_fan_project tss_cli project list tss_cli project query --project_name cli_fan_project #Add training data tss_cli signal list --project_name cli_fan_project tss_cli signal import --project_name cli_fan_project --signal_name ON --file_path C:\tss\data\fan_state_monitoring_3channel\train\train_on.csv --label_id 1 --delimiter " " tss_cli signal import --project_name cli_fan_project --signal_name OFF --file_path C:\tss\data\fan_state_monitoring_3channel\train\train_off.csv --label_id 2 --delimiter " " tss_cli signal import --project_name cli_fan_project --signal_name FRICTION --file_path C:\tss\data\fan_state_monitoring_3channel\train\train_friction.csv --label_id 3 --delimiter " " tss_cli signal import --project_name cli_fan_project --signal_name CLOG --file_path C:\tss\data\fan_state_monitoring_3channel\train\train_clog.csv --label_id 4 --delimiter " " #Check training data tss_cli signal query --project_name cli_fan_project --signal_id 1 #Start training the model. It will print out an opt_ID number, which the first time you run it will be "1". tss_cli optimization start --project_name cli_fan_project -qs --opt_name cli_fan_opt --signals 1 2 3 4 #Get opt_id number while training is running tss_cli optimization list --project_name cli_fan_project #Check how far along the training is and get ranking of models to choose a result_ids tss_cli optimization progress --project_name cli_fan_project --opt_id 1   #Can stop the training if feel like have enough results tss_cli optimization stop --project_name cli_fan_project --opt_id 1 #Get the result_ids of the best result. It will also be the top ID when checking the progress above. In this case will use 48 tss_cli optimization results --project_name cli_fan_project --opt_id 1 #Get Execution Time estimate for that model tss_cli library time_estimate --project_name cli_fan_project --opt_id 1 --result_id 48   #Get Label Names tss_cli signal list --project_name cli_fan_project   #Emulate the library on test data tss_cli emulation launch --project_name cli_fan_project --opt_id 1 --result_ids 48 --test_file_info "1" C:\tss\data\fan_state_monitoring_3channel\test\test_on.csv " " --test_file_info "2" C:\tss\data\fan_state_monitoring_3channel\test\test_off.csv " " --test_file_info "3" C:\tss\data\fan_state_monitoring_3channel\test\test_friction.csv " " --test_file_info "4" C:\tss\data\fan_state_monitoring_3channel\test\test_clog.csv " " #Create a TSS library tss_cli library compile --project_name cli_fan_project --opt_id 1 --result_id 48 --save_path "C:\tss\" --arch "cortex-m33" --toolchain "GCC" #Create a TSS example project tss_cli library sample_project --project_name cli_fan_project --opt_id 1 --result_id 48 --save_path "C:\tss" --arch "cortex-m33" --toolchain "GCC"  

When deploying a custom built model to replace the default models in MCUXpresso SDK examples, there are several modifications that need to be made as described in the eIQ Neutron NPU hands-on labs. Here are some common issues and error messages that you might encounter when using a new custom model with the SDK examples and how to solve them. If there is an issue not covered here, then please make a new thread to discuss that issue.    “Didn't find op for builtin opcode ‘<operator_name>’” Need to add that operator to MODEL_GetOpsResolver function found in source\model\model_name_ops_npu.cpp A full list of operators used by a model that can be copy-and-pasted into that file is automatically generated by Neutron Converter Tool with the --dump-header-file-output option at the top of the resulting header file.  Make sure to also increase the size of the static array s_microOpResolver to match the number of operators   “resolver size is too small” Need to increase the size of the static array s_microOpResolver in MODEL_GetOpsResolver function found in source\model\model_name_ops_npu.cpp to match the number of operators     “Failed to resize buffer” The scratch memory buffer for the model is too small for the model and needs to be increased. The size of the memory buffer is set with the kTensorArenaSize variable found in the model data header file The size of this buffer can be estimated when running the Neutron Converter tool in the "Total data" field but this estimate is often slightly smaller than the actual amount used. The actual TensorArenaSize buffer required can be determined when running the model by calling s_interpreter->arena_used_bytes(); which is printed out to the serial terminal in the eIQ MCUXpresso SDK examples. The recommendation is to use the estimation but increase by ~10%, run the model, and then use the arena_used_bytes API to determine the true amount of scratch memory required.   “Internal Neutron NPU driver error 281b in model prepare!” or “Incompatible Neutron NPU microcode and driver versions!” Ensure the version of the eIQ Neutron Converter Tool used to convert the model is the correct one that is compatible with the NPU libraries used by the SDK project.  See this Community Post for how to update the eIQ Neutron libraries.     Camera colors are incorrect on FRDM-MCXN947 board See this Community post for more details on using a camera with the FRDM-MCXN947 Modify solder jumpers SJ16, SJ26, and SJ27 on the back of board to move them to the left (dashed line side) to connect camera signals properly.             This modification will disable Ethernet functionality on the board due to a signal conflict with EZH D0 and ENET_TXCLK. If your project needs both camera and Ethernet functionality, then only move SJ16 and SJ26 to the left (dashed line side) and then connect a wire from P1_4 (J9 pin 😎 to the left side of R58. Then in the pin_mux.c file in the project, instead of using PORT1_PCR4 for EZH_Camera_D0, use PORT3_PCR0.                           

  This guide helps you to: Get familiar with NPU inside the MCX Know eIQ ML examples included in SDK. Summarize related application notes and demo projects on GitHub Prerequisites: Windows 10 development PC. FRDM-MCXN947 board NXP’s  MCUXpresso IDE is installed on the development PC Generate and download FRDM-MCXN947 SDK package from the web-based MCUXpresso SDK builder. Introduction to NPU The MCXN94x and MCXN54x are based on dual high-performance Arm® Cortex®-M33 cores running at up to 150 MHz, it has 2MB of on-chip Flash with optional full ECC RAM and an integrated proprietary NPU. The integrated NPU delivers up to 40x faster machine learning (ML) throughput compared to a CPU core, enabling it to spend less time awake and reducing overall power consumption. The architecture provides power and performance-optimized NPUs integrated with NXP's very wide portfolio of microcontrollers and applications processors. The eIQ Neutron NPUs offer support for a wide variety of neural network types such as CNN, RNN, TCN, and Transformer networks and more. ML application development with the eIQ Neutron NPU is fully supported by the eIQ machine learning software development environment. The NPU used in MCXN94 is Neutron N1-16, its block diagram is shown in the below figure. The eIQ Neutron N1-16 NPU found inside the MCXN94 has 4 compute pipes and each compute pipe contains 4 INT8 MAC (Multiply Accumulate) blocks for a total of 16 MAC blocks. This means that MCXN94 could execute 4.8G(150MHz * 4 * 4 * 2) INT8 operations per second. The MCUXpresso Software Development Kit (MCUXpresso SDK) provides a comprehensive software package with a pre-integrated TensorFlow Lite for Microcontrollers (TFLM). The Neutron library is integrated into TFLM as well. The following table shows the operators which are supported by the NPU. Operator Operator input type MCXN947/MCXN548 NPU ADD Float No Uint8(PTQ) No Int8(PCQ) Yes AVERAGE_POOL_2D Float No Uint8(PTQ) No Int8(PCQ) Yes CONV_2D Float No Uint8(PTQ) No Int8(PCQ) Yes DEPTHWISE_CONV_2D Float No Uint8(PTQ) No Int8(PCQ) Yes FULLY_CONNECTED Float No Uint8(PTQ) No Int8(PCQ) Yes UNIDIRECTIONAL_SEQUENCE_ LSTM Float No Uint8(PTQ) No Int8(PCQ) No LOGISTIC (Sigmoid) Float No Uint8(PTQ) No Int8(PCQ) Yes MAX_POOL_2D Float No Uint8(PTQ) No Int8(PCQ) Yes MUL Float No Uint8(PTQ) No Int8(PCQ) No SOFTMAX Float No Uint8(PTQ) No Int8(PCQ) No SVDF Float No Uint8(PTQ) No Int8(PCQ) No Note: PTQ — Per-tensor quantized (asymmetric 8-bit quantization). PCQ — Per-channel quantized (symmetric 8-bit quantization). For more information please refer to eIQ TensorFlow Lite User's Guide.pdf in middleware/eiq/doc of SDK. eIQ ML examples included in SDK. Download SDK and select FRDM-MCXN947 in MCUxpresso SDK Builder, Remember to select eIQ middleware. Then open MCUXpresso, it’s convenient to install the SDK by dragging and dropping the file into the SDK installation window of the MCUXpresso IDE. Import SDK examples into the workspace: There are 7 ML examples included in SDK: Here are the descriptions of the eIQ examples: eIQ example Description Hardware requirements tflm_cifar10 CIFAR10 example based on TensorFlow Lite Micro, recognizes a static image FRDM-MCXN947 USB type-c cable tflm_kws Keyword spotting example based on TensorFlow Lite Micro recognizes a static WAV audio FRDM-MCXN947 USB type-c cable tflm_label_image Label 1000 classes of images based on TensorFlow Lite Micro FRDM-MCXN947 USB type-c cable mpp_camera_mobilenet_view_tflm Label camera images based on TensorFlow Lite Micro FRDM-MCXN947 LCD: MikroElektronika TFT Proto 5" OV7670 module USB type-c cable mpp_camera_ultraface_view_tflm Face detection using the camera as the source, based on TensorFlow Lite Micro FRDM-MCXN947 LCD: MikroElektronika TFT Proto 5" OV7670 module USB type-c cable mpp_camera_view A simple camera preview pipeline. FRDM-MCXN947 LCD: MikroElektronika TFT Proto 5" OV7670 module USB type-c cable tflm_modelrunner TFLite Model Benchmark example for Microcontrollers. FRDM-MCXN947 RJ45 Network cable For additional information and guidance, kindly refer to the README file located in the doc folder that accompanies each example. Summary of related application notes There are two application notes available that provide information on advanced usage of NPU. How to Integrate Customer ML Model to NPU on MCXN94x Face Detection demo with NPU accelerated on MCXN947. Please find the notes on the NXP website Summary of demo projects on GitHub There are five demos on nxp-appcodehub from GitHub: Multiple Face detection on FRDM-MCXN947 Multiple person detection on FRDM-MCXN947 Label CIFAR10 images on FRDM-MCXN947 Fashion-MNIST recognition on FRDM-MCXN947 NPU vs TensorFLM benchmark on MCX