GenAI Efficiency is About More than Models

Kurt is Professor of the Graduate School in the Berkeley AI Research (BAIR) Lab of the Department of Electrical Engineering and Computer Science at University of California, Berkeley. After a distinguished career in Electronic Design Automation where he won many Best Paper awards, as well as a Most Influential Paper of the Decade award, Kurt turned his attention to efficient Machine Learning, and, later, efficient Deep Learning. Kurt is probably best known for his “Squeeze” family of Neural Nets that helped to pioneer the use of Deep Learning at the edge, but he has also collaborated to scale the training of Neural Nets to 1000’s of processors. Kurt’s research is currently moving beyond single-model optimization to efficiently harnessing the power of sophisticated systems of LLMs. He is a Life Fellow of the IEEE.