Course Outline
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
Requirements
- 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
Delivery Options
Private Group Training
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- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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