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Course Outline
Introduction to TinyML and Embedded AI
- Characteristics of TinyML model deployment
- Constraints in microcontroller environments
- Overview of embedded AI toolchains
Model Optimization Foundations
- Understanding computational bottlenecks
- Identifying memory-intensive operations
- Baseline performance profiling
Quantization Techniques
- Post-training quantization strategies
- Quantization-aware training
- Evaluating accuracy vs resource trade-offs
Pruning and Compression
- Structured and unstructured pruning methods
- Weight sharing and model sparsity
- Compression algorithms for lightweight inference
Hardware-Aware Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Memory mapping and dataflow considerations
Benchmarking and Validation
- Latency and throughput analysis
- Power and energy consumption measurements
- Accuracy and robustness testing
Deployment Workflows and Tools
- Using TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse pipelines
- Testing and debugging on real hardware
Advanced Optimization Strategies
- Neural architecture search for TinyML
- Hybrid quantization-pruning approaches
- Model distillation for embedded inference
Summary and Next Steps
Requirements
- An understanding of machine learning workflows
- Experience with embedded systems or microcontroller-based development
- Familiarity with Python programming
Audience
- AI researchers
- Embedded ML engineers
- Professionals working on resource-constrained inference systems
21 Hours
