Low-Power AI: Optimizing Edge AI for Energy-Efficient Devices Training Course
Low-power AI concentrates on optimizing artificial intelligence models to operate efficiently on resource-constrained, battery-powered edge devices.
This instructor-led, live training (available online or onsite) targets advanced-level AI engineers, embedded developers, and hardware engineers who aim to implement AI models on low-power devices while minimizing energy consumption.
Upon completing this training, participants will be able to:
- Grasp the challenges associated with running AI on energy-efficient devices.
- Optimize neural networks for low-power inference.
- Apply quantization, pruning, and model compression techniques.
- Deploy AI models on edge hardware with minimal power usage.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
- An understanding of deep learning models
- Experience with embedded systems or AI deployment
- Basic knowledge of model optimization techniques
Audience
- AI engineers
- Embedded developers
- Hardware engineers
Open Training Courses require 5+ participants.
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Course - Advanced Edge AI Techniques
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