Texas Instruments (TI) Edge AI is an advanced framework designed to facilitate the embedding of deep learning models into devices at the edge of the network. It is particularly tailored for environments where computing power is limited and efficiency is crucial. TI's Edge AI platform aims to address the high computational demands of deep learning technologies by optimizing and accelerating the inference process on TI’s embedded devices.
The platform supports a heterogeneous execution environment, allowing deep neural networks (DNNs) to run across various processing units such as Cortex-A based MPUs, TI's cutting-edge C7x digital signal processors (DSPs), and the Deep Neural Network accelerator (MMA). This execution strategy ensures that each part of the deep learning model operates on the most suitable processing unit, maximizing performance and energy efficiency.
TI's Edge AI not only makes it possible to bring the power of deep learning to edge devices but also simplifies the development process for engineers and developers. With comprehensive software products and developer-friendly resources, TI provides tools and documentation to ensure a seamless experience from development to deployment.
Our documentation landing pages are the following:
- https://www.ti.com/edgeai : Technology page summarizing TI’s edge AI software/hardware products
- https://github.com/TexasInstruments/edgeai : Landing page for developers to understand overall software and tools offering
Embedded inference of Deep Learning models is quite challenging - due to high compute requirements. TI’s Edge AI comprehensive software product help to optimize and accelerate inference on TI’s embedded devices. It supports heterogeneous execution of DNNs across cortex-A based MPUs, TI’s latest generation C7x DSP and DNN accelerator (MMA).
TI's Edge AI solution simplifies the whole product life cycle of DNN development and deployment by providing a rich set of tools and optimized libraries.
The figure below provides a high level summary of the relevant tools:
The table below provides detailed explanation of each of the tools:
| Category | Tool/Link | Purpose | IS NOT |
|---|---|---|---|
| Model training & associated tools | edgeai-modelzoo | Model Zoo |
|
| ditto | Model optimization tools | Model optimization tools |
- Does not support Tensorflow |
| ditto | edgeai-torchvision |
Training repositories for various tasks |
- Does not support Tensorflow |
| Inference (and compilation) Tools | edgeai-tidl-tools | To get familiar with model compilation and inference flow |
- Does not support benchmarking accuracy of models using TIDL with standard datasets, for e.g. - accuracy benchmarking using MS COCO dataset for object detection models. Please refer to edgeai-benchmark for the same. |
| ditto | edgeai-benchmark | Bring your own model and compile, benchmark and generate artifacts for deployment on SDK with camera, inference and display (using edgeai-gst-apps) |
|
| Integrated environment for training and compilation | Edge AI Studio: Model Analyzer | Browser based environment to allow model evaluation with TI EVM farm |
- Does not support Camera, Display and inference based end-to-end pipeline development. Please refer Edge AI SDK for such usage |
| ditto | Edge AI Studio: Model Composer | GUI based Integrated environment for data set capture, annotation, training, compilation with connectivity to TI development board |
- Does not support Bring Your Own Model workflow |
| ditto | Model Maker | Command line Integrated environment for training & compilation |
- Does not support Bring Your Own Model workflow |
| Edge AI Software Development Kit | Devices & SDKs | SDK to develop end-to-end AI pipeline with camera, inference and display |
Bring your own model (BYOM) workflow:
Train your own model (TYOM) workflow:
Bring your own data (BYOD) workflow:
Read some of our Technical publications
Issue tracker for Edge AI Studio is listed in its landing page.
Issue tracker for TIDL: Please include the tag TIDL (as you create a new issue, there is a space to enter tags, at the bottom of the page).
Issue tracker for edge AI SDK Please include the tag EDGEAI (as you create a new issue, there is a space to enter tags, at the bottom of the page).
Issue tracker for ModelZoo, Model Benchmark & Deep Neural Network Training Software: Please include the tag MODELZOO (as you create a new issue, there is a space to enter tags, at the bottom of the page).
- [2023-Dec] Updated link to Model Optimization Tools
- [2023-May] Documentation update and restructure.
- [2023-March] Several of these repositories have been updated
- [2022-April] Several of these repositories have been updated
- [2021-August] Several of our repositories are being moved from git.ti.com to github.com
- [2021-December-21] Several of our repositories are being updated in preparation for the 8.1 (08_01_00_xx) release. These include edgeai-tidl-tools, edgeai-benchmark, edgeai-modelzoo and edgeai-torchvision. A new version of PROCESSOR-SDK-LINUX-SK-TDA4VM that corresponds to this will be available in a few days.
- [2022-April-5] Several of the repositories are being updated in preparation for the 8.2 (08_02_00_xx) release.
Please see the LICENSE file for more information about the license under which this landing repository is made available. The LICENSE file of each repository mentioned here is inside that repository.