Edge AI Implementation Guide - A comprehensive guide from the overview and issues to how to implement AI functions on edge devices! - <Free>

Recently, there has been a trend to process AI on the edge due to the increased data volume, communication charges, and latency that come with implementing AI on the cloud. By utilizing edge AI, it is possible to realize AI functions that do not rely on the cloud. However, compared to cloud AI, hardware resources are limited, and in reality, there are many cases where projects cannot move forward smoothly. Many people are probably worried about the following:

"I want to implement the AI model I have prepared on an edge device, but I don't know how to do it."
"I want to easily run the AI model I have prepared on an embedded processor."
"It seems like it would need to be tailored to the hardware, and we can't allocate the manpower."

With the wide range of edge AI devices being released, some people may be wondering which device to use for testing.

In this seminar, we will explain the issues and implementation methods of edge AI implementation using NXP Semiconductors' i.MX embedded processor as an example. We hope you will understand how easy it is to start verification. We will also introduce how to implement an AI model developed in the NVIDIA environment on the i.MX embedded processor, so please join us.

2024/09/25(Wed) 11:00-12:00 (Registration 10:45 -)

Online (via Zoom Webinar)

NXP Semiconductor: i.MX Applications Processor

[eIQ Beginner's Guide 0] eIQ® Machine Learning (ML) Software Development Environment Overview