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Machine Learning on MCUs

About This Training

This session provides an overview of machine learning on i.MX RT crossover MCUs, with a focus on hands-on labs for eIQ^® inference with Glow, DeepViewRT, and TensorFlow Lite for MCUs.

This session is part of the AI and Machine Learning Training Academy developed to help you get to market faster with NXP's eIQ ML software development environment.

Training Outline

Machine Learning with i.MX RT Crossover MCUs

eIQ ML Software on i.MX RT Crossover MCUs: SW and HW Requirements

eIQ Inference with TensorFlow Lite for MCUs: Overview

eIQ Inference with TensorFlow Lite for MCUs: Hands-On Lab

eIQ Inference with Glow Neural Network Compiler: Overview

eIQ Inference with Glow Neural Network Compiler: Hands-On Lab

eIQ Inference with DeepViewRT for MCUs: Overview

What You Will Learn

Overview of the i.MX RT Crossover MCU Family
Hardware requirements for the i.MX RT hands-on labs
How to download eIQ, the eIQ Toolkit, Glow and other eIQ software enablement
Details on TensorFlow Lite for Microcontrollers on i.MX RT MCUs
Use transfer learning to retrain an image classification model to recognize new types of images
Run that model on an i.MX RT1170-EVK
Details on the Glow Neural Network Compiler on i.MX RT devices
Quantize and compile a model using the Glow compiler tools
Run the compiled model on an MIMXRT1170-EVK
Details on DeepViewRT on i.MX RT devices