SDAA185 February   2026

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1 Introduction
    1. 1.1 NPU Definition and Purpose
    2. 1.2 Key Capabilities
    3. 1.3 Technical Limitations
  5. 2 Development Flow Overview
    1. 2.1 Model Development Phase
    2. 2.2 Model Compilation Phase
    3. 2.3 Application Integration Phase
  6. 3 Example Model Creation (Python)
    1. 3.1 Model Selection Rationale
    2. 3.2 Model Architecture Design
    3. 3.3 Training Details
      1. 3.3.1 Development Environment Setup
      2. 3.3.2 Dataset Generation
      3. 3.3.3 Model Training Configuration
      4. 3.3.4 Quantization-Aware Training Process
  7. 4 Quantization for Embedded Platform
    1. 4.1 Quantization Approaches: QAT versus PTQ
      1. 4.1.1 Post-Training Quantization (PTQ)
      2. 4.1.2 Quantization-Aware Training (QAT)
    2. 4.2 Quantization Frameworks and Wrapper Modules
      1. 4.2.1 Generic Wrappers for CPU Quantization
      2. 4.2.2 TINPU Wrappers for NPU Hardware Acceleration
  8. 5 Validating the Model
    1. 5.1 Two-Phase Training Strategy
      1. 5.1.1 Initial Training Phase
      2. 5.1.2 Fine-Tuning Phase
    2. 5.2 Training Phase Comparison
    3. 5.3 Validation Results and Metrics
  9. 6 Testing the Model
    1. 6.1 Inference Setup and Methodology
      1. 6.1.1 Generic User Testing Approach
    2. 6.2 Testing Results and Visual Analysis
      1. 6.2.1 Visual Performance Assessment
    3. 6.3 Quantitative Performance Metrics
  10. 7 Moving the Model to TI MCU (C2000 – F28P55x) [Beginner Level]
  11. 8 Moving the Model to TI MCU (C2000 – F28P55x) [Developer Level]
    1. 8.1 Compilation Prerequisites
      1. 8.1.1 Required TI Software Components
      2. 8.1.2 Environment Setup Process
    2. 8.2 Configuration File Setup
      1. 8.2.1 Configuration File Structure
        1. 8.2.1.1 Models Requiring Dequantization Flag
      2. 8.2.2 Special Configuration for Regression Models
        1. 8.2.2.1 Output Dequantization Flag
        2. 8.2.2.2 Compiler Constants Modification
        3. 8.2.2.3 Compilation Dictionary Update
    3. 8.3 Compilation Process Flow
      1. 8.3.1 Launching the Compilation
      2. 8.3.2 Compilation Phases
      3. 8.3.3 Common Issues to Watch For
  12. 9 Setting up the MCU Project
    1. 9.1 Creating a CCS Project for NPU Applications
    2. 9.2 Understanding the NPU Interface
      1. 9.2.1 Key Interface Components
      2. 9.2.2 Basic Usage Pattern
  13. 10Testing the Model in the Embedded Environment
    1. 10.1 Visual Performance Assessment
    2. 10.2 Quantitative Performance Metrics
  14. 11NPU Integration in a Real-Time Signal Chain
    1. 11.1 Application Block Diagram
    2. 11.2 Application Code Implementation
    3. 11.3 Hardware Components Utilized
    4. 11.4 Hardware Validation Results
      1. 11.4.1 Input Signal Characteristics
      2. 11.4.2 Neural Network Output Analysis
  15. 12Key Design Decisions and Impact
    1. 12.1 NPU Handling of Numbers
      1. 12.1.1 Integer-Only Architecture
      2. 12.1.2 Working with Negative and Floating-Point Values
    2. 12.2 Supported Neural Network Layers and Constraints
      1. 12.2.1 Supported Layer Types
        1. 12.2.1.1 Convolution Layers
        2. 12.2.1.2 Other Core Layers
        3. 12.2.1.3 Flexibilities
    3. 12.3 Model Complexity and Size Limitations
      1. 12.3.1 Memory Constraints and Model Size
      2. 12.3.2 Optimization Process and Performance Trade-offs
  16. 13Benchmarks
    1. 13.1 Model Performance Comparison
      1. 13.1.1 128 - Neuron Model
      2. 13.1.2 64 - Neuron Model
      3. 13.1.3 16 - Neuron Model
      4. 13.1.4 Reference Benchmark
    2. 13.2 Performance Analysis
      1. 13.2.1 Model Selection Trade-offs
      2. 13.2.2 CPU versus NPU Performance
    3. 13.3 Pipeline Stage Timing Measurements
  17. 14Summary
    1. 14.1 Key Capabilities and Constraints
    2. 14.2 Development Workflow
    3. 14.3 Model Design Considerations
    4. 14.4 Implementation Challenges and Solutions
    5. 14.5 Broader Applications
  18. 15References

5.3 Validation Results and Metrics

The validation process employed a simple methodology to maintain objective performance assessment:

  • Validation Dataset: 20% of the generated data held out from training.
  • Multiple Metrics: Comprehensive assessment using complementary evaluation metrics.
  • Quantization Simulation: Validation performed using the same quantization scheme as the target hardware.

The quantization-aware trained Sine_64 model achieved good performance metrics, as expected for a simple ML model, that validate its effectiveness for NPU deployment:

  • Validation Loss is measure by Mean Squared Error (MSE). Mean Squared Error measures the average of squared differences between predicted values (ŷi) and actual values (yi). Lower values indicate better model performance.
Equation 1. MSE=1/n∑0n(yi-ŷi)2
  • Mean Absolute Error measures the average absolute difference between predicted and actual values. Unlike MSE, this does not square the errors, which is less sensitive to outliers.
Equation 2. MAE=1/n∑0n|yi-ŷi|
  • R² indicates how well the model explains the variance in the data compared to using the mean as a predictor. Values range from 0 to 1, with 1 representing perfect prediction. A validation R² score indicates the model explanation of variance of given data.
Equation 3. R2=1-(∑0nyi-ŷi2)/(∑0nyi-ȳ2)

Variables:

  • yi = Actual (true) sine value for the ith sample
  • ŷi = Predicted sine value from the neural network for the ith sample
  • ȳ = Mean of all actual sine values in the validation dataset
Table 5-2 Training Validation Metrics
MetricMetricSignificance
Validation Loss0.00038Extremely low mean squared error indicating high prediction accuracy
Validation MAE0.01305Mean absolute error of ~1.3% across the sine range (-1 to +1)
Validation R² Score0.9993Nearly perfect coefficient of determination, indicating the model explains 99.93% of the variance in sine values

These metrics demonstrate several key achievements:

  • High Precision: Despite quantization constraints, the model achieves sub-percent accuracy.
  • Consistent Performance: Strong R² score indicates reliable prediction across the entire input range.
  • Quantization Resilience: Minimal performance degradation despite integer-only operations.
  • Deployment Readiness: Metrics validate the model's suitability for NPU implementation.