Texas Instruments has a multitude of resources available for machine learning at the edge (Edge AI). These are spread across ti.com, dev.ti.com (TI Developer Zone + Edge AI Studio), and github. The majority of these resources are linked here as a one-stop-shop for finding information to aid various stages of Edge AI development and evaluation.
Resources common across Edge AI AM6xA SoCs
|Primary Edge AI page
|Edge AI Studio
|Model analyzer and model composer available here for cloud-based training and model evaluation
|Edge AI Academy
|Intended for new developers in the Edge AI space. Covers overview of the topics and how TI is solving problems in this space
|Edge AI Demos
List of existing open-source and 3rd party demos
|Main github page for Edge AI
Links to all other github resources are available here. Most useful github repos are listed below
|Github repo for model compilation, testing
Compile, test, debug, evaluate models
|Github repo for model training
Train, label, compile models
|Github repo for model evaluation on EVM
Run end-to-end pipelines with gstraemer for input capture, model inference, and output to display. See /forks for domain-specific application examples and models.
Please see comments for device-specific resources.