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Course Outline
Introduction to Low-Power AI
- Overview of AI in embedded systems.
- Challenges of AI deployment on low-power devices.
- Energy-efficient AI applications.
Model Optimization Techniques
- Quantization and its impact on performance.
- Pruning and weight sharing.
- Knowledge distillation for model simplification.
Deploying AI Models on Low-Power Hardware
- Using TensorFlow Lite and ONNX Runtime for edge AI.
- Optimizing AI models with NVIDIA TensorRT.
- Hardware acceleration with Coral TPU and Jetson Nano.
Reducing Power Consumption in AI Applications
- Power profiling and efficiency metrics.
- Low-power computing architectures.
- Dynamic power scaling and adaptive inference techniques.
Case Studies and Real-World Applications
- AI-powered battery-operated IoT devices.
- Low-power AI for healthcare and wearables.
- Smart city and environmental monitoring applications.
Best Practices and Future Trends
- Optimizing edge AI for sustainability.
- Advancements in energy-efficient AI hardware.
- Future developments in low-power AI research.
Summary and Next Steps
Requirements
- A solid understanding of deep learning models.
- Prior experience with embedded systems or AI deployment.
- Fundamental knowledge of model optimization techniques.
Audience
- AI engineers.
- Embedded developers.
- Hardware engineers.
21 Hours
Testimonials (1)
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