Tiny ML Tensorlab User Guide
Welcome to the Tiny ML Tensorlab documentation! This comprehensive guide covers Texas Instruments’ end-to-end AI toolchain for developing, training, optimizing, and deploying machine learning models on resource-constrained microcontrollers.
Note
This documentation is for Tiny ML Tensorlab version 1.3.0.
Quick Links
New to Tiny ML Tensorlab? Start here with our quickstart guide and hello world example.
Browse practical examples including arc fault detection, motor bearing fault classification, and more.
Prefer a GUI? Use our no-code web platform to train and deploy models.
Find your target TI MCU and learn about NPU acceleration options.
What is Tiny ML Tensorlab?
Tiny ML Tensorlab is Texas Instruments’ complete solution for bringing AI to microcontrollers. The toolchain enables you to:
Train machine learning models for time series and image classification tasks
Optimize models using quantization (2-bit, 4-bit, 8-bit) for embedded deployment
Compile models to run efficiently on TI MCUs, with optional NPU acceleration
Deploy models using Code Composer Studio (CCS)
Supported Task Types
Task Type |
Description |
|---|---|
Time Series Classification |
Categorize time-series data into discrete classes (e.g., fault detection, activity recognition) |
Time Series Regression |
Predict continuous values from time-series inputs (e.g., torque estimation) |
Time Series Forecasting |
Predict future values based on historical patterns (e.g., temperature prediction) |
Anomaly Detection |
Identify abnormal patterns using autoencoder-based models (e.g., equipment monitoring) |
Image Classification |
Categorize images into classes (e.g., visual inspection, digit recognition) |
Documentation Structure
- User Guide
Start with the Introduction to understand the toolchain architecture, then follow the Installation guide to set up your environment.
- Task Types
Learn about the different Supported Task Types supported and choose the right one for your application.
- Working with Data
The Bring Your Own Data (Bring Your Own Data) section explains dataset formats and preparation.
- Target Devices
Browse Supported Devices to find specifications and capabilities for 20+ TI MCUs.
- Examples & Applications
The Examples & Applications section provides ready-to-run configurations for common applications.
- Advanced Features
Explore Advanced Features like Neural Architecture Search, quantization, and analysis tools.
- Deployment
The Device Deployment section covers CCS integration and running models on devices.
- Edge AI Studio
Prefer a GUI? See Edge AI Studio Model Composer for our no-code web platform.
- Extending the Toolchain
The Bring Your Own Model (Bring Your Own Model) section covers adding custom models.