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Description of  Efficient Processing of Deep Neural Networks

by Wujie Zhang (Author) This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metricssuch as energy-efficiency, throughput, and latencywithout sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing a description and taxonomy of hardware architectural approaches for designing DNN accelerators key metrics for evaluating and comparing different designs features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas. About the Author Vivienne Sze received a B.A.Sc. (Hons) degree in Electrical Engineering from the University of Toronto, Toronto, ON, Canada, in 2004, and S.M. and Ph.D. degrees in Electrical Engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, in 2006 and 2010, respectively. In 2011, she received the Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT. She is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department. Her research interests include energy-aware signal processing algorithms, and low-power circuit and system design for portable multimedia applications including computer vision, deep learning, autonomous navigation, image processing, and video compression. Prior to joining MIT, she was a Member of Technical Staff in the Systems and Applications R&D Center at Texas Instruments (TI), Dallas, TX, where she designed low-power algorithms and architectures for video coding. She also represented TI in the JCT-VC committee of ITUT and ISO/IEC standards body during the development of High Efficiency Video Coding (HEVC), which received a Primetime Engineering Emmy Award. Within the committee, she was the primary coordinator of the core experiment on coefficient scanning and coding, and she chaired/vice-chaired several ad hoc groups on entropy coding. She is a co-editor of High Efficiency Video Coding (HEVC): Algorithms and Architectures (Springer, 2014). Prof. Sze is a recipient of the inaugural ACM-W Rising Star Award, the 2019 Edgerton Faculty Achievement Award at MIT, the 2018 Facebook Faculty Award, the 2018 & 2017 Qualcomm Faculty Award, the 2018 & 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Research Program (YIP) Award, the 2016 3M Non-Tenured Faculty Award, the 2014 DARPA Young Faculty Award, the 2007 DAC/ISSCC Student Design Contest Award and a co-recipient of the 2018 VLSI Best Student Paper Award, the 2017 CICC Outstanding Invited Paper Award, the 2016 IEEE Micro Top Picks Award, and the 2008 ASSCC Outstanding Design Award. She currently serves on the technical program committee for the International Solid-State Circuits Conference (ISSCC) and the SSCS Advisory Committee (AdCom). She has served on the technical program committees for VLSI Circuits Symposium, Micro and the Conference on Machine Learning and Systems (MLSys), as a guest editor for the IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), and as a Distinguished Lecturer for the IEEE Solid-State Circuits Society (SSCS). Prof. Sze was Program Co-chair of the 2020 Conference on Machine Learning and Systems (MLSys) and teaches the MIT Professional Education course on Designing Efficient Deep Learning Systems. Yu-Hsin Chen received a B.S. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in 2009, and M.S. and Ph.D. degrees in Electrical Engineering and Computer Science (EECS) from Massachusetts Institute of Technology (MIT), Cambridge, MA, in 2013 and 2018, respectively. He received the 2018 Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT and the 2019 ACM SIGARCH/IEEE-CS TCCA Outstanding Dissertation Award. He is currently a Research Scientist at Facebook focusing on hardware/software co-design to enable on-device AI for AR/VR systems. Previously, he was a Research Scientist in Nvidia's Architecture Research Group. He was the recipient of the 2015 Nvidia Graduate Fellowship, 2015 ADI Outstanding Student Designer Award, and 2017 IEEE SSCS Predoctoral Achievement Award. His work on the dataflows for CNN accelerators was selected as one of the Top Picks in Computer Architecture in 2016. He also co-taught a tutorial on "Hardware Architectures for Deep Neural Networks" at MICRO-49, ISCA2017, MICRO-50, and ISCA2019. Tien-Ju Yang received a B.S. degree in Electrical Engineering from National Taiwan University (NTU), Taipei, Taiwan, in 2010, and an M.S. degree in Electronics Engineering from NTU in 2012. Between 2012 and 2015, he worked in the Intelligent Vision Processing Group, MediaTek Inc., Hsinchu, Taiwan, as an engineer. He is currently a Ph.D. candidate in Electrical Engineering and Computer Science at Massachusetts Institute of Technology, Cambridge, MA, working on efficient deep neural network design. His research interest spans the area of deep learning, computer vision, machine learning, image/video processing, and VLSI system design. He won first place in the 2011 National Taiwan University Innovation Contest. He also co-taught a tutorial on "Efficient Image Processing with Deep Neural Networks" at IEEE International Conference on Image Processing 2019. Joel S. Emer received B.S. (Hons.) and M.S. degrees in Electrical Engineering from Purdue University, West Lafayette, IN, USA, in 1974 and 1975, respectively, and a Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign, Champaign, IL, USA, in 1979. He is currently a Senior Distinguished Research Scientist with Nvidia's Architecture Research Group, Westford, MA, USA, where he is responsible for exploration of future architectures and modeling and analysis methodologies. He is also a Professor of the Practice at the Massachusetts Institute of Technology, Cambridge, MA, USA. Previously, he was with Intel, where he was an Intel Fellow and the Director of Microarchitecture Research. At Intel, he led the VSSAD Group, which he had previously been a member of at Compaq and Digital Equipment Corporation. Over his career, he has held various research and advanced development positions investigating processor micro-architecture and developing performance modeling and evaluation techniques. He has made architectural contributions to a number of VAX, Alpha, and X86 processors and is recognized as one of the developers of the widely employed quantitative approach to processor performance evaluation. He has been recognized for his contributions in the advancement of simultaneous multithreading technology, processor reliability analysis, cache organization, pipelined processor organization and spatial architectures for deep learning. Dr. Emer is a Fellow of the ACM and IEEE and a member of the NAE. He has been a recipient of numerous public recognitions. In 2009, he received the Eckert-Mauchly Award for lifetime contributions in computer architecture. He received the Purdue University Outstanding Electrical and Computer Engineer Alumni Award and the University of Illinois Electrical and Computer Engineering Distinguished Alumni Award in 2010 and 2011, respectively. His 1996 paper on simultaneous multithreading received the AC