SDAA429 June   2026 MSPM0G5187

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1Introduction
  5. 2MSPM0G5187 with TinyEngine NPU
  6. 3Edge AI Toolchains
    1. 3.1 TI Edge AI Studio
    2. 3.2 TI Tiny ML Tensorlab
    3. 3.3 TI Neural Network Compiler
  7. 4Edge AI Application: Digit Recognition
    1. 4.1 LeNet-5 Variant CNN Model
    2. 4.2 NPU/CPU Performance Comparison
  8. 5Edge AI Application: Waveform Classifier
    1. 5.1 Feature Extraction
    2. 5.2 Time-Series Classification Model
    3. 5.3 Model Memory Considerations
    4. 5.4 NPU/CPU Performance Comparison
  9. 6Summary
  10. 7References

3.2 TI Tiny ML Tensorlab

The Tiny ML Tensorlab is Texas Instruments’ complete design for bringing AI to microcontrollers, which enables users 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)

This supports the following Machine Learning (ML) task types:

Table 3-1 Supported Task Types
Task TypeDescription
Time Series ClassificationCategorize time-series data into discrete classes (for example, fault detection, activity recognition)
Time Series RegressionPredict continuous values from time-series inputs (for example, torque estimation)
Time Series ForecastingPredict future values based on historical patterns (for example, temperature prediction)
Anomaly DetectionIdentify abnormal patterns using autoencoder-based models (for example, equipment monitoring)
Image ClassificationCategorize images into classes (for example, visual inspection, digit recognition)