Introduction to TinyML

• Basic programming knowledge (Python or C/C++)
• Familiarity with machine learning concepts (recommended but not required)
• Understanding of embedded systems (optional but helpful)

Audience

• Engineers
• Data scientists
• AI enthusiasts

TinyML is the application of machine learning on resource-constrained microcontrollers and embedded devices.

This instructor-led, live training (online or onsite) is aimed at beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

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

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.

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?
• The significance of machine learning on microcontrollers
• Comparison between traditional AI and TinyML
• Overview of hardware and software requirements

Setting Up the TinyML Environment

• Installing Arduino IDE and setting up the development environment
• Introduction to TensorFlow Lite and Edge Impulse
• Flashing and configuring microcontrollers for TinyML applications

Building and Deploying TinyML Models

• Understanding the TinyML workflow
• Training a simple machine learning model for microcontrollers
• Converting AI models to TensorFlow Lite format
• Deploying models onto hardware devices

Optimizing TinyML for Edge Devices

• Reducing memory and computational footprint
• Techniques for quantization and model compression
• Benchmarking TinyML model performance

TinyML Applications and Use Cases

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

TinyML Challenges and Future Trends

• Hardware limitations and optimization strategies
• Security and privacy concerns in TinyML
• Future advancements and research in TinyML

Summary and Next Steps