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

6 Summary

This application note provides a comprehensive introduction to the Edge AI solution based on the MSPM0G5187 microcontroller, covering the hardware characteristics, software toolchains, and representative application examples.

Two applications are validated to demonstrate the platform capability: the digit recognition solution achieves ~99% classification accuracy, with the hardware NPU delivering 15x lower inference latency and 93% better energy efficiency over the CPU-based implementation; the waveform classifier achieves 100% classification accuracy, with the NPU delivering 8x lower inference latency and 91% better energy efficiency.

These results confirm that the MSPM0G5187, powered by the integrated TinyEngine NPU, is a compelling platform for deploying AI efficiently on resource-constrained embedded devices.