Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Foundations of TinyML and Embedded AI
- Key attributes of TinyML model deployment
- Limitations within microcontroller environments
- Survey of embedded AI toolchains
Basics of Model Optimization
- Analyzing computational bottlenecks
- Recognizing memory-heavy operations
- Establishing baseline performance profiles
Quantization Methods
- Strategies for post-training quantization
- Implementation of quantization-aware training
- Measuring the trade-offs between accuracy and resource usage
Pruning and Model Compression
- Techniques for structured and unstructured pruning
- Applications of weight sharing and model sparsity
- Compression algorithms designed for lightweight inference
Hardware-Centric Optimization
- Deploying models on ARM Cortex-M architectures
- Optimizing for DSP and hardware accelerator extensions
- Considerations for memory mapping and dataflow
Performance Benchmarking and Validation
- Analysis of latency and throughput
- Measurement of power and energy consumption
- Testing for accuracy and system robustness
Deployment Processes and Tooling
- Leveraging TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models into Edge Impulse workflows
- Conducting testing and debugging on physical hardware
Advanced Optimization Tactics
- Neural architecture search applied to TinyML
- Combining quantization and pruning strategies
- Using model distillation for embedded inference
Conclusion and Path Forward
Requirements
- Working knowledge of machine learning workflows
- Experience with embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals developing resource-constrained inference systems
