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Course Outline
Introduction to TinyML and Embedded AI
- Key characteristics of TinyML model deployment
- Challenges within microcontroller environments
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Recognizing computational bottlenecks
- Identifying operations that demand significant memory
- Establishing baseline performance profiles
Quantization Techniques
- Strategies for post-training quantization
- Quantization-aware training processes
- Evaluating the balance between accuracy and resource usage
Pruning and Compression
- Methods for structured and unstructured pruning
- Weight sharing and achieving model sparsity
- Compression algorithms designed for lightweight inference
Hardware-Aware Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Addressing memory mapping and dataflow considerations
Benchmarking and Validation
- Analysis of latency and throughput
- Measurements of power and energy consumption
- Testing for accuracy and robustness
Deployment Workflows and Tools
- Utilizing TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse pipelines
- Testing and debugging on actual hardware
Advanced Optimization Strategies
- Neural architecture search tailored for TinyML
- Hybrid approaches combining quantization and pruning
- Model distillation for embedded inference
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Experience with embedded systems or development using microcontrollers
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals focused on inference systems with resource constraints
21 Hours
