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Duration 21 hours
Course Outline
Introduction to Low-Power AI
- Overview of AI integration in embedded systems
- Operational challenges for deploying AI on resource-constrained devices
- Applications of energy-efficient AI technologies
Model Optimization Strategies
- Quantization methods and their effects on model performance
- Structural pruning and weight-sharing mechanisms
- Knowledge distillation for simplifying complex models
Deployment of AI Models on Low-Power Hardware
- Leveraging TensorFlow Lite and ONNX Runtime for edge AI for government use cases
- Enhancing AI model efficiency using NVIDIA TensorRT
- Hardware acceleration utilizing Coral TPU and Jetson Nano platforms
Minimizing Power Consumption in AI Operations
- Power profiling methodologies and efficiency assessment metrics
- Low-power computing architectures for sustainable operations
- Dynamic power scaling and adaptive inference protocols
Case Studies and Practical Applications
- AI-driven battery-operated IoT devices for government infrastructure
- Low-power AI solutions in healthcare and wearable technologies
- Smart city initiatives and environmental monitoring systems
Best Practices and Emerging Trends
- Optimizing edge AI for long-term sustainability
- Advances in energy-efficient AI hardware design
- Future directions in low-power AI research and development
Summary and Recommended Next Steps
Requirements
- Proficiency in deep learning model architectures
- Practical experience with embedded systems or AI deployment processes
- Fundamental knowledge of model optimization methodologies
Target Audience
- AI engineers
- Embedded developers
- Hardware engineers
Testimonials (1)
That we can cover advance topic and work with real-life example
