テクノロジーデイズ - トレーニング教材

ディスカッション

You need to get a lot of things right to build a truly secure system. Join this session to learn how the EdgeLock™ secure enclave that’s built into our i.MX 8ULP applications processors protects the entire system against attacks. Best of all, it simplifies complex implementations so you’re less likely to make errors when you configure security. With the secure enclave, you can achieve security goals with less security expertise, freeing you to focus on new ways to differentiate your application. Presenter: Lawrence Case, Systems Engineer and Security Architect, NXP

TensorFlow® Lite, ArmNN, and GLOW are popular open-source machine learning inference frameworks for mobile and IoT devices. In this session, you’ll learn how to use TensorFlow Lite, and ArmNN on NXP i.MX 8 MPU-class devices in Linux, and how to take advantage of not only Arm® Cortex®-A CPU cores, but also dedicated on-chip GPU and NPU accelerators. For NXP i.MX RT MCU-class devices we will introduce two approaches: 1)TensorFlow Lite for Microcontrollers with CMSIS-NN kernel implementation optimized for Cortex-M cores, and 2) GLOW, a neural network compiler, which generates code “Ahead of Time” for Cortex-M cores and DSP. Presenter: Robert Kalmar, Machine Learning SW Engineer, NXP

Learn about the safety related elements of i.MX 8/8X hardware and how NXP is going the extra step with their functional safety concepts. The i.MX 8/8X is designed as a Safety Element out of Context (SEooC) microprocessor (MPU) and is intended for multiple use cases within the vehicle. Join us to see how the i.MX 8/8X can be used in dedicated manners for these specific safety-critical applications, like automotive digital instrument clusters, where critical information and alerts need to be assessed and analyzed at all times.

This presentation will first highlights some basic training techniques, such as data augmentation, but the primary focus will be on various ways to deploy neural network frameworks and classical machine learning algorithms, and most importantly, utilizing a variety of open source tools and techniques. We will show how these techniques fit in with some real use cases such as object recognition and anomaly detection. The hands-on session portion of the class will allow attendees to take a trained neural network model and deploy it on a i.MX 8M. Attendees will utilize the Linux development environment and implement an end-to-end use case.

Join this session to get a glimpse of where you will see i.MX 8/8X in the near future and why graphics, ML, AI, video, image processing, vision, connectivity, audio and voice functions make the i.MX 8 series ideal for Automotive and Industrial applications. Learn about the i.MX 8/8X families that are comprised of common subsystems establishing an unmatched range of performance scaling with pin-compatible options and the highest level of software reuse. In addition, learn more about the upcoming i.MX 8DualMax product line to help customers meet additional cost performance scaling across the i.MX 8 family.

This presentation will provide an introduction to the impressive lineup of i.MX 8M family of products. The session will cover the product features, media, graphics and machine learning use cases, enablement and ecosystem. We will follow the presentation with a live demonstration based on the i.MX 8M Plus applications processor. Presenters: Marcel Baessler, Arrow Franck Roux, Products and Solutions Marketing Manager, NXP

As memory interface data rates increase, so does the need for ensuring proper margins have been designed into the system’s usage of that interface. One way Micron has studied the system’s usage of the memory interface is through Timing & Signal Analysis (TSA). A TSA attaches physical hardware to the memory device to observe a subset of the memory interface as operated by the system. Another approach to memory interface is Virtual TSA (vTSA). This method uses the memory controller’s training algorithms to provide margin information without the use of physical test hardware. This method allows characterization of the entire memory interface. This session will compare and contrast the two different methods, and will ultimately demonstrate the value of integrating vTSA tools on the system platform. We intend to reference actual results from the latest i.MX 8 QXP board.

Machine learning can performed on a wide range of device categories - from MCUs with Arm® Cortex®-M4 and M7 cores to complex SoCs with high-end A-class cores, GPUs, DSPs, and dedicated machine learning accelerators. The first step is learning how to utilize proper training techniques for model development, but beyond that how to generate optimized inference engines that can be used to perform classifications, anomaly detection, predictions, and other types of decisions. This presentation highlights some basic training techniques, such as data augmentation, but the primary focus will be on various ways to deploy neural network frameworks and classical machine learning algorithms, and most importantly, utilizing a variety of open source tools and techniques. We will show how these techniques fit in with some real use cases such as object recognition and anomaly detection.

テクノロジーデイズ - トレーニング教材

Technology Days - Training Material

Training Sessions Material

Home

Vehicle Networking

AI and Machine Learning

AI and Machine Learning; Edge Computing

AI and Machine Learning; Voice

Connectivity (UWB, 5G, Wi-Fi 6)

Embedded Applications Software; Human Machine Interface (HMI)

Embedded Software

Human Machine Interface (HMI)

Motor Control

Security

Enabling Technologies

Voice; Human Machine Interface (HMI)

Factory Automation

Wearable; Human Machine Interface (HMI)

Home Control & Security; Development Software & Tools

Automotive

Edge Intelligence

Industrial

IoT Smart Home

Hardware & Software Solutions

Automotive

Edge Intelligence

Industrial

Mobile, Wearable & Computing

IoT Smart Home

Hardware & Software Solutions

Automotive

IoT Smart City

Edge Intelligence

Industrial

Mobile, Wearable & Computing

IoT Smart Home

Hardware & Software Solutions

Station F

Singapore

Automotive

Edge Intelligence

IoT Smart Home

Mobile, Wearable & Computing

Industrial

Hardware & Software Solutions

Automotive

Communication Infrastructure

Edge Intelligence

Industrial

IoT Smart Home

Hardware & Software Solutions

Mobile, Wearable & Computing

Automotive

Edge Intelligence

Hardware & Software Solutions

Industrial

IoT Smart City

IoT Smart Home

Automotive

Automotive

Hardware and Software Solutions

Industrial

IoT Smart Home

Mobile, Wearable, and Computing

Automotive

Edge Intelligence

Hardware & Software Solutions

Industrial

IoT Smart City

IoT Smart Home

Mobile, Wearable & Computing

Automotive

Artificial Intelligence, Enabling Technologies, Security & Solutions

Smart Machinery & Industrial Automation

Smarter World Solutions & Multimode Connectivity

Artificial Intelligence, Enabling Technologies, Security & Solutions

Smart Machinery & Industrial Automation

Smarter World Solutions & Multimode Connectivity

Automotive

Artificial Intelligence, Enabling Technologies, Security & Solutions