1. Introduction

This section introduces Tiny ML Tensorlab, Texas Instruments’ comprehensive AI toolchain for developing machine learning models on microcontrollers.

Contents

• 1.1. What is Tiny ML Tensorlab?
  • 1.1.1. Overview
  • 1.1.2. Target Applications
  • 1.1.3. Key Capabilities
  • 1.1.4. Repository Structure
  • 1.1.5. Workflow Summary
  • 1.1.6. Next Steps
• 1.2. System Architecture
  • 1.2.1. High-Level Architecture
  • 1.2.2. Component Details
  • 1.2.3. Data Flow
  • 1.2.4. Configuration System
  • 1.2.5. Integration Points
• 1.3. Terminology
  • 1.3.1. General ML Terms
  • 1.3.2. Tiny ML Tensorlab Terms
  • 1.3.3. Device & Hardware Terms
  • 1.3.4. Configuration Terms
  • 1.3.5. Data Terms
  • 1.3.6. Model Size Conventions
  • 1.3.7. Abbreviations

1.4. Overview

Tiny ML Tensorlab enables end-to-end machine learning workflows for embedded systems:

1. Data Preparation - Format and preprocess your datasets

2. Model Training - Train neural networks optimized for MCUs

3. Model Optimization - Quantize models for efficient deployment

4. Compilation - Generate device-ready code using TI’s Neural Network Compiler

5. Deployment - Run inference on TI microcontrollers

Whether you’re detecting electrical faults, classifying motor bearing conditions, or building custom AI applications, Tiny ML Tensorlab provides the tools you need.