In-/Near-Memory Computing

This book provides a structured introduction of the key concepts and techniques that enable in-/near-memory computing. For decades, processing-in-memory or near-memory computing has been attracting growing interest due to its potential to break the memory wall. Near-memory computing moves compute logic near the memory, and thereby reduces data movement. Recent work has also shown that certain memories can morph themselves into compute units by exploiting the physical properties of the memory cells, enabling in-situ computing in the memory array. While in- and near-memory computing can circumvent overheads related to data movement, it comes at the cost of restricted flexibility of data representation and computation, design challenges of compute capable memories, and difficulty in system and software integration. Therefore, wide deployment of in-/near-memory computing cannot be accomplished without techniques that enable efficient mapping of data-intensive applications to such devices, without sacrificing accuracy or increasing hardware costs excessively. This book describes various memory substrates amenable to in- and near-memory computing, architectural approaches for designing efficient and reliable computing devices, and opportunities for in-/near-memory acceleration of different classes of applications.

Daichi Fujiki received his B.E. degree from Keio University, Tokyo, Japan, in 2016 and his M.S.Eng. degree from the University of Michigan, Ann Arbor, MI, in 2017. He is currently pursuing a Ph.D. in Computer Science and Engineering with the University of Michigan, Ann Arbor, MI. He is a member of the Mbits Research Group, Computer Engineering Laboratory (CELAB), University of Michigan, which develops in-situ compute memory architectures and custom acceleration hardware for bioinformatics workloads.

Xiaowei Wang received his B.Eng. degree in Electronic Information Science and Technology from Tsinghua University, Beijing, China, in 2015. He received his M.S. degree in Computer Science and Engineering from the University of Michigan, Ann Arbor, MI, in 2017, where he is currently pursuing a Ph.D. in Computer Science and Engineering. He is advised by Prof. Reetuparna Das. His research interests include domain-specific architectures for machine learning, in-memory computing, and hardware/software co-design.

Arun Subramaniyan received his B.E (Hons.) in Electrical and Electronics from the Birla Institute of Technology and Science (BITS-Pilani), India in 2015. He is currently a Ph.D. student at the University of Michigan, advised by Prof. Reetuparna Das. His dissertation research focuses on developing efficient algorithms and customized computing systems for precision health. He is also interested in in-memory computing architectures and hardware reliability. His work has been recognized by UM's Precision Health Scholars Award, a Rackham International Students Fellowship, an IEEE Micro Top Picks Award, and a Best Paper Award in CODESCISSS.