Deep In-memory Architectures for Machine Learning
by:Mingu Kang, Sujan Gonugondla, et al.
pages 页数：174 pages
Publisher Finelybook 出版社：Springer; 1st ed. 2020 edition (January 31, 2020)
From the Back Cover
This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware.
Describes deep in-memory architectures for AI systems from first principles, covering both circuit design and architectures;
Discusses how DIMAs pushes the limits of energy-delay product of decision-making machines via its intrinsic energy-SNR trade-off;
Offers readers a unique Shannon-inspired perspective to understand the system-level energy-accuracy trade-off and robustness in such architectures;
Illustrates principles and design methods via case studies of actual integrated circuit prototypes with measured results in the laboratory;
Presents DIMA’s various models to evaluate DIMA’s decision-making accuracy, energy, and latency trade-offs with various design parameter.
About the Author
Mingu Kang received the B.S. and M.S. degrees in Electrical and Electronic Engineering from Yonsei University, Seoul, South Korea, in 2007 and 2009, respectively, and the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana–Champaign, Champaign, IL, USA, in 2017. From 2009 to 2012, he was with the Memory Division, Samsung Electronics, Hwaseong, South Korea, where he was involved in the circuit and architecture design of phase change memory (PRAM). Since 2017, he has been with the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, where he designs machine learning accelerator architectures. His current research interests include low-power integrated circuits, architectures, and systems for machine learning, signal processing, and neuromorphic computing.
Sujan Gonugondla received the B.Tech and M.Tech. degrees in Electrical Engineering from the Indian Institute of Technology Madras, Chennai, India, in 2014. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, Champaign, IL, USA. His current research interests include low-power integrated circuits specifically algorithm hardware co-design for machine learning systems on resource constrained environments. Sujan
Gonugondla is a recipient of the Dr. Ok Kyun Kim Fellowship 2018-19 from the ECE department at the University of Illinois at Urbana-Champaign and the ADI Outstanding Student Designer Award 2018.