NXP eIQ™Machine Learning Software Development Environment for i.MX Applications Processors User Manual

2022-04-26
●Introduction
■Machine Learning (ML) is a computer science domain that has its roots in the 1960s. ML provides algorithms capable of finding patterns and rules in data. ML is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of ML is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
■In 2010, the so-called deep learning started. It is a fast-growing subdomain of ML, based on Neural Networks (NN). Inspired by the human brain, deep learning achieved state-of-the-art results in various tasks; for example, Computer Vision (CV) and Natural Language Processing (NLP). Neural networks are capable of learning complex patterns from millions of examples. A huge adaptation is expected in the embedded world, where NXP is the leader. NXP created eIQ machine learning software for i.MX applications processors, a set of ML tools which allows developing and deploying ML applications on the i.MX8 family of devices.
■This document provides guidance for the supported ML software for the i.MX family. The document is divided into separate sections, starting with the NXP eIQ introduction, the Yocto installation guide, and the step-by step guide for running all supported DNN and non-DNN examples.

NXP

Machine Learning Software

More

i.MX Applications Processors ]

More

User's Guide

More

More

Please see the document for details

More

More

English Chinese Chinese and English Japanese

06/2019

Rev. 2

UM11226

1.9 MB

- The full preview is over. If you want to read the whole 37 page document,please Sign in/Register -
  • +1 Like
  • Add to Favorites

Recommend

All reproduced articles on this site are for the purpose of conveying more information and clearly indicate the source. If media or individuals who do not want to be reproduced can contact us, which will be deleted.

Contact Us

Email: