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Norman P. Jouppi: Google's Next Generation TPU Domain-Specific Architecture for Deep Neural Networks
Virtual Computer Architecture Seminar
Abstract: The ending of Moore's Law leaves domain-specific architectures as the future of computing. A trailblazing example is the Google's tensor processing unit (TPU), first deployed in 2015, and that provides services today for more than one billion people. It runs deep neural networks (DNNs) 15 to 30 times faster with 30 to 80 times better energy efficiency than contemporary CPUs and GPUs in similar technologies. See: https://cacm.acm.org/magazines/2018/9/230571-a-domain-specific-architecture-for-deep-neural-networks/fulltext
Biography: Norman P. Jouppi is a Distinguished Hardware Engineer at Google. He is known for his innovations in computer memory systems, including stream prefetch buffers, victim caching, multi-level exclusive caching, and development of the CACTI tool for modeling memory timing, area, and power. He has been the principal architect and lead designer of several microprocessors, contributed to the architecture and design of graphics accelerators, and extensively researched video, audio, and physical telepresence. His innovations in microprocessor design have been adopted in many high-performance microprocessors. His recent research has investigated the impact of emerging technologies such as non-volatile memory and nanophotonics on computer systems. Jouppi received his Ph.D. in electrical engineering from Stanford University in 1984, and a master of science in electrical engineering from Northwestern University in 1980.