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Duration 21 hours
Course Outline
Foundations of TinyML and Embedded AI
- Key characteristics of deploying TinyML models
- Limits and constraints in microcontroller environments
- Introduction to embedded AI toolchains
Basics of Model Optimization
- Identifying computational bottlenecks
- Detecting memory-intensive operations
- Establishing baseline performance metrics
Quantization Methods
- Strategies for post-training quantization
- Implementing quantization-aware training
- Balancing accuracy against resource usage
Pruning and Model Compression
- Techniques for structured and unstructured pruning
- Utilizing weight sharing and model sparsity
- Algorithms for efficient, lightweight inference
Hardware-Specific Optimization
- Deploying models on ARM Cortex-M architectures
- Leveraging DSP and accelerator extensions
- Considerations for memory mapping and data flow
Performance Benchmarking and Validation
- Analyzing latency and throughput
- Measuring power and energy consumption
- Testing for accuracy and system robustness
Deployment Strategies and Tools
- Using TensorFlow Lite Micro for embedded applications
- Integrating TinyML models with Edge Impulse pipelines
- Conducting tests and debugging on physical hardware
Advanced Optimization Techniques
- Applying neural architecture search for TinyML
- Combining quantization and pruning for hybrid approaches
- Employing model distillation for embedded inference
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Practical experience in embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals specializing in resource-constrained inference systems
