Low-Power AI: Optimizing Edge AI for Energy-Efficient Devices Training Course
Energy-efficient AI centers on refining artificial intelligence models to operate effectively on devices with limited resources and battery constraints.
This instructor-led live training (available online or onsite) targets advanced AI engineers, embedded system developers, and hardware specialists seeking to deploy AI models on low-power devices while reducing energy usage.
Upon completing this training, participants will be equipped to:
- Grasp the challenges associated with running AI on energy-efficient devices.
- Optimize neural networks for low-power inference tasks.
- Apply techniques such as quantization, pruning, and model compression.
- Deploy AI models on edge hardware while maintaining minimal power consumption.
Training Format
- Engaging lectures and interactive discussions.
- Extensive exercises and practical practice sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To arrange a customized training session, please contact us.
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
- Fundamental understanding of deep learning models
- Experience working with embedded systems or AI deployment
- Basic knowledge of model optimization techniques
Target Audience
- AI engineers
- Embedded developers
- Hardware engineers
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Low-Power AI: Optimizing Edge AI for Energy-Efficient Devices Training Course - Enquiry
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That we can cover advance topic and work with real-life example
Ruben Khachaturyan - iris-GmbH infrared & intelligent sensors
Course - Advanced Edge AI Techniques
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