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Course Outline
Introduction to Energy-Efficient AI
- The significance of sustainability in AI
- Overview of energy consumption in machine learning
- Case studies of energy-efficient AI implementations
Compact Model Architectures
- Understanding model size and complexity
- Techniques for designing small yet effective models
- Comparing different model architectures for efficiency
Optimization and Compression Techniques
- Model pruning and quantization
- Knowledge distillation for smaller models
- Efficient training methods to reduce energy usage
Hardware Considerations for AI
- Selecting energy-efficient hardware for training and inference
- The role of specialized processors like TPUs and FPGAs
- Balancing performance and power consumption
Green Coding Practices
- Writing energy-efficient code
- Profiling and optimizing AI algorithms
- Best practices for sustainable software development
Renewable Energy and AI
- Integrating renewable energy sources in AI operations
- Data center sustainability
- The future of green AI infrastructure
Lifecycle Assessment of AI Systems
- Measuring the carbon footprint of AI models
- Strategies for reducing environmental impact throughout the AI lifecycle
- Case studies on lifecycle assessment in AI
Policy and Regulation for Sustainable AI
- Understanding global standards and regulations
- The role of policy in promoting energy-efficient AI
- Ethical considerations and societal impact
Project and Assessment
- Developing a prototype using small language models in a chosen domain
- Presentation of the energy-efficient AI system
- Evaluation based on technical efficiency, innovation, and environmental contribution
Summary and Next Steps
Requirements
- Solid understanding of deep learning concepts
- Proficiency in Python programming
- Experience with model optimization techniques
Audience
- Machine learning engineers
- AI researchers and practitioners
- Environmental advocates within the tech industry
21 Hours