Deploying AI on Microcontrollers with TinyML

• Experience with embedded systems programming
• Familiarity with Python or C/C++ programming
• Basic knowledge of machine learning concepts
• Understanding of microcontroller hardware and peripherals

Audience

• Embedded systems engineers
• AI developers

TinyML enables AI models to run efficiently on microcontrollers and edge devices with low power consumption.

This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to TinyML and Edge AI

• What is TinyML?
• Advantages and challenges of AI on microcontrollers
• Overview of TinyML tools: TensorFlow Lite and Edge Impulse
• Use cases of TinyML in IoT and real-world applications

Setting Up the TinyML Development Environment

• Installing and configuring Arduino IDE
• Introduction to TensorFlow Lite for microcontrollers
• Using Edge Impulse Studio for TinyML development
• Connecting and testing microcontrollers for AI applications

Building and Training Machine Learning Models

• Understanding the TinyML workflow
• Collecting and preprocessing sensor data
• Training machine learning models for embedded AI
• Optimizing models for low-power and real-time processing

Deploying AI Models on Microcontrollers

• Converting AI models to TensorFlow Lite format
• Flashing and running models on microcontrollers
• Validating and debugging TinyML implementations

Optimizing TinyML for Performance and Efficiency

• Techniques for model quantization and compression
• Power management strategies for edge AI
• Memory and computation constraints in embedded AI

Practical Applications of TinyML

• Gesture recognition using accelerometer data
• Audio classification and keyword spotting
• Anomaly detection for predictive maintenance

Security and Future Trends in TinyML

• Ensuring data privacy and security in TinyML applications
• Challenges of federated learning on microcontrollers
• Emerging research and advancements in TinyML

Summary and Next Steps