SDAA429A June   2026  – October 2026 MSPM0G5187

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1 Introduction
  5. 2 MSPM0G5187 with TinyEngine NPU
  6. 3 Edge AI Toolchains
    1. 3.1 TI Edge AI Studio
    2. 3.2 TI Tiny ML Tensorlab
    3. 3.3 TI Neural Network Compiler
  7. 4 Rapid Prototyping Through TI Edge AI Studio
    1. 4.1 Project Setup
    2. 4.2 Data Capture
      1. 4.2.1 Hardware Setup
      2. 4.2.2 Feature Overview
    3. 4.3 Data Import
      1. 4.3.1 Classification Task
      2. 4.3.2 Regression Task
      3. 4.3.3 Forecasting Task
      4. 4.3.4 Annotation List
    4. 4.4 Data Annotation
    5. 4.5 Model Training
      1. 4.5.1 Training Parameter
      2. 4.5.2 Toolchain Dependency
    6. 4.6 Performance Reports
    7. 4.7 Model Export
  8. 5 Advanced Development Through TI Tiny ML Tensorlab
    1. 5.1 Environment Setup
    2. 5.2 Data Acquisition
      1. 5.2.1 Data Retrieval
      2. 5.2.2 Data Validation
    3. 5.3 Model Architecture Iterative Refactoring
      1. 5.3.1 Baseline Implementation
      2. 5.3.2 Refactoring With PyTorch Built-in Modules
      3. 5.3.3 Data Pipeline Optimization
      4. 5.3.4 Transition to CNN Architecture
    4. 5.4 Model Export
    5. 5.5 Quantization-Aware Training
    6. 5.6 Model Compilation
  9. 6 Edge AI Application: Digit Recognition
    1. 6.1 LeNet-5 Variant CNN Model
    2. 6.2 NPU/CPU Performance Comparison
  10. 7 Edge AI Application: Waveform Classifier
    1. 7.1 Feature Extraction
    2. 7.2 Time-Series Classification Model
    3. 7.3 Model Memory Considerations
    4. 7.4 NPU/CPU Performance Comparison
  11. 8 Summary
  12. 9 References
  13. 10Revision History
Application Note

Using MSPM0G5187 in Edge Artificial Intelligence Applications