TinyML: Running AI on Ultra-Low-Power Edge Devices

• An understanding of embedded systems and microcontrollers
• Experience with AI or machine learning fundamentals
• Basic knowledge of C, C++, or Python programming

Audience

• Embedded engineers
• IoT developers
• AI researchers

TinyML is revolutionizing AI by enabling ultra-low-power machine learning on microcontrollers and resource-constrained edge devices.

This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

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

• What is TinyML?
• Why run AI on microcontrollers?
• Challenges and benefits of TinyML

Setting Up the TinyML Development Environment

• Overview of TinyML toolchains
• Installing TensorFlow Lite for Microcontrollers
• Working with Arduino IDE and Edge Impulse

Building and Deploying TinyML Models

• Training AI models for TinyML
• Converting and compressing AI models for microcontrollers
• Deploying models on low-power hardware

Optimizing TinyML for Energy Efficiency

• Quantization techniques for model compression
• Latency and power consumption considerations
• Balancing performance and energy efficiency

Real-Time Inference on Microcontrollers

• Processing sensor data with TinyML
• Running AI models on Arduino, STM32, and Raspberry Pi Pico
• Optimizing inference for real-time applications

Integrating TinyML with IoT and Edge Applications

• Connecting TinyML with IoT devices
• Wireless communication and data transmission
• Deploying AI-powered IoT solutions

Real-World Applications and Future Trends

• Use cases in healthcare, agriculture, and industrial monitoring
• The future of ultra-low-power AI
• Next steps in TinyML research and deployment

Summary and Next Steps