Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs.
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Lizhong Chen is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He received his Ph.D. in Computer Engineering and M.S. in Electrical Engineering from the University of Southern California in 2014 and 2011, respectively, and B.S. in Electrical Engineering from Zhejiang University in 2009. His research interests are in the board area of computer architecture, interconnection networks, GPUs, machine learning, hardware accelerators, and emerging IoT technologies. Dr. Chen is the recipient of National Science Foundation (NSF) CAREER Award, several Best Paper Awards/Nominations at major architecture conferences, Chu Kochen Award (the highest honor from Zhejiang University), and an inductee of the HPCA Hall of Fame. He is also the founder and organizer of the Annual International Workshop on AI-assisted Design for Architecture (AIDArc), held in conjunction with ISCA. Dr. Chen is currently serving on the editorial board of IEEE Transactions on Computers (TC) and, in the past, has served as a program committee member in major computer architecture conferences (e.g., ISCA, HPCA, MICRO, DAC, ICS, IPDPS, IISWC), reviewer for several IEEE and ACM journals (e.g., TC, TPDS, TVLSI, TCAD, TACO), and panelist of multiple NSF panels related to computer systems architecture. He is a Senior Member of IEEE and ACM.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs. 144 pp. Englisch. Bestandsnummer des Verkäufers 9783031006425
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