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Course Outline
Introduction to Energy-Efficient AI
- The importance 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 into 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
- Strong grasp of deep learning concepts.
- Proficiency in Python programming.
- Experience with model optimization techniques.
Target Audience
- Machine learning engineers.
- AI researchers and practitioners.
- Environmental advocates within the technology sector.
21 Hours
