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Course Outline
Introduction to TinyML and Embedded AI for Government
- Characteristics of deploying TinyML models in microcontroller environments
- Constraints within microcontroller settings
- Overview of embedded AI toolchains for government use
Foundations of Model Optimization
- Understanding computational bottlenecks in TinyML models
- Identifying memory-intensive operations within embedded systems
- Baseline performance profiling techniques for government applications
Quantization Techniques for Government Models
- Post-training quantization strategies for efficient deployment
- Implementing quantization-aware training to enhance model efficiency
- Evaluating the trade-offs between accuracy and resource usage in government applications
Pruning and Compression Methods
- Structured and unstructured pruning techniques for government models
- Weight sharing and model sparsity optimization for efficient inference
- Compression algorithms to support lightweight inference in government systems
Hardware-Aware Optimization for Government Systems
- Deploying models on ARM Cortex-M systems for government use
- Optimizing models for DSP and accelerator extensions to enhance performance
- Memory mapping and dataflow considerations in government applications
Benchmarking and Validation for Government Models
- Latency and throughput analysis for government systems
- Power and energy consumption measurements to ensure efficiency
- Accuracy and robustness testing to meet government standards
Deployment Workflows and Tools for Government
- Utilizing TensorFlow Lite Micro for embedded deployment in government projects
- Integrating TinyML models with Edge Impulse pipelines for government applications
- Testing and debugging on real hardware to ensure reliability in government systems
Advanced Optimization Strategies for Government Use
- Neural architecture search tailored for TinyML in government contexts
- Hybrid quantization-pruning approaches for enhanced performance in government models
- Model distillation techniques to optimize embedded inference for government applications
Summary and Next Steps for Government Applications
Requirements
- A comprehensive understanding of machine learning workflows for government applications
- Practical experience with embedded systems or microcontroller-based development for government projects
- Proficiency in Python programming for government use cases
Audience
- AI researchers working in the public sector
- Embedded ML engineers focused on government initiatives
- Professionals engaged in developing resource-constrained inference systems for government operations
21 Hours
