A Decade of Machine Learning Accelerators: Lessons Learned and Carbon Footprint
Explore a comprehensive lecture on machine learning accelerators, their evolution, and environmental impact delivered by David Patterson, a distinguished engineer at Google and professor emeritus at UC Berkeley. Delve into ten key lessons learned from a decade of domain-specific architecture development for deep neural networks, including insights on rapid DNN growth, evolving workloads, and memory bottlenecks. Examine the critical 4Ms (Model, Machine, Mechanism, Map) that can significantly reduce ML training energy consumption and carbon emissions. Gain valuable perspectives on sustainable machine learning practices, the importance of accurate emissions reporting in ML research, and the potential for ML to positively impact various fields while addressing climate change concerns.