Handwritten Digit Recognition Using TensorFlow Lite on i.MX RT1060 MCUs
This application note focuses on handwritten digit recognition on embedded systems through deep learning, using i.MX RT1060 MCUs, MCUXpresso SDK and eIQ™ technology.
The NXP® eIQ™ machine learning (ML) software development environment enables the use of ML algorithms on NXP EdgeVerse™ microcontrollers and microprocessors, including i.MX RT crossover MCUs, and i.MX family application processors. eIQ ML software includes a ML workflow tool called eIQ Toolkit, along with inference engines, neural network compilers and optimized libraries. This software leverages open-source and proprietary technologies and is fully integrated into our MCUXpresso SDK and Yocto development environments, allowing you to develop complete system-level applications with ease.
A machine learning compiler that enables ahead-of-time compilation by converting neural networks into object files, which are then converted into binary images for increased performance and smaller memory footprint.
Image recognition for label identification using the i.MX RT1060 and a TensorFlow Lite model.
A high-level look at the main benefits of the Controller Area Network (CAN) standard.