9. Device Deployment

This section guides you through deploying trained models on TI microcontrollers using Code Composer Studio (CCS).

Contents

• 9.1. CCS Integration Guide
  • 9.1.1. Prerequisites
  • 9.1.2. Compilation Output
  • 9.1.3. Creating a CCS Project
  • 9.1.4. Integration Code
  • 9.1.5. Memory Placement
  • 9.1.6. Linker Command File
  • 9.1.7. Interrupt-Based Inference
  • 9.1.8. Timing and Profiling
  • 9.1.9. Debugging
  • 9.1.10. Build Configurations
  • 9.1.11. Example Project Structure
  • 9.1.12. Common Issues
  • 9.1.13. Testing on Hardware
  • 9.1.14. Next Steps
• 9.2. NPU Device Deployment
  • 9.2.1. NPU-Enabled Devices
  • 9.2.2. NPU Compilation
  • 9.2.3. NPU Model Requirements
  • 9.2.4. NPU Compilation Artifacts
  • 9.2.5. NPU Initialization
  • 9.2.6. NPU Inference Code
  • 9.2.7. NPU Memory Management
  • 9.2.8. NPU Performance
  • 9.2.9. NPU Power Considerations
  • 9.2.10. NPU Debugging
  • 9.2.11. NPU Error Handling
  • 9.2.12. CCS Project Setup for NPU
  • 9.2.13. Example: Arc Fault on F28P55 NPU
  • 9.2.14. Troubleshooting NPU Issues
  • 9.2.15. Next Steps
• 9.3. Non-NPU Deployment
  • 9.3.1. Non-NPU Devices
  • 9.3.2. Configuration
  • 9.3.3. Model Selection
  • 9.3.4. CPU Inference Performance
  • 9.3.5. Compilation Artifacts
  • 9.3.6. CCS Project Setup
  • 9.3.7. Basic Integration
  • 9.3.8. Optimizing CPU Inference
  • 9.3.9. Memory Optimization
  • 9.3.10. Power Optimization
  • 9.3.11. Real-Time Considerations
  • 9.3.12. Device-Specific Notes
  • 9.3.13. Example: Vibration Monitoring on MSPM0G3507
  • 9.3.14. Comparison: NPU vs Non-NPU
  • 9.3.15. Next Steps

9.4. Deployment Overview

After training and compilation, Tiny ML Tensorlab produces artifacts that you can integrate into your CCS project:

Output Artifacts

File	Description
mod.a	Compiled model library
tvmgen_default.h	Header file with model inference API
test_vector.c	Test vectors for device validation
user_input_config.h	Feature extraction configuration for device

Deployment Steps

1. Train and compile your model using Tiny ML Tensorlab

2. Open Code Composer Studio

3. Import or create a project for your target device

4. Copy the compiled artifacts to your project

5. Build, flash, and debug

The specific steps vary depending on whether your device has an NPU:

• NPU Device Deployment - For F28P55, AM13E2, MSPM0G5187

• Non-NPU Deployment - For all other devices

9.5. Prerequisites

• Code Composer Studio installed (version 20.x or later recommended)

• Appropriate SDK installed:

  • C2000 devices: C2000Ware SDK

  • MSPM0 devices: MSPM0 SDK

• TI compiler tools configured (environment variables set)