Tensorflow Lite for Microcontrollers Training Course

Introduction

• Differentiating Microcontrollers from Microprocessors
• Microcontrollers Tailored for Machine Learning Applications

Overview of TensorFlow Lite Features for Government

• On-device Machine Learning Inference for Enhanced Efficiency
• Addressing Network Latency Issues
• Managing Power Constraints Effectively
• Ensuring Data Privacy and Security

Constraints of a Microcontroller in Public Sector Applications

• Energy Consumption and Physical Size Considerations
• Limited Processing Power, Memory, and Storage Capabilities
• Restricted Operational Functions

Getting Started with Microcontroller Development for Government

• Setting Up the Development Environment
• Executing a Simple Hello World Program on the Microcontroller

Creating an Audio Detection System for Government Use

• Acquiring a TensorFlow Model Suitable for Government Applications
• Converting the Model to a TensorFlow Lite FlatBuffer

Serializing the Code for Government Systems

• Transforming the FlatBuffer into a C Byte Array for Efficient Storage and Execution

Working with Microcontroller's C++ Libraries for Government Projects

• Coding the Microcontroller to Meet Government Specifications
• Gathering Data in Compliance with Public Sector Standards
• Performing Inference on the Controller to Ensure Real-time Analysis

Verifying the Results for Government Applications

• Conducting a Unit Test to Validate the End-to-End Workflow for Government Use

Creating an Image Detection System for Government Operations

• Classifying Physical Objects from Image Data for Enhanced Public Sector Functionality
• Developing a TensorFlow Model from Scratch to Meet Specific Government Requirements

Deploying an AI-Enabled Device in the Field for Government Use

• Running Inference on a Microcontroller in Real-world Government Scenarios

Troubleshooting Common Issues for Government Applications

Summary and Conclusion for Government Users