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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
- 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
21 Hours
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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