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.

本書提供了關鍵概念和技術的結構化介紹，以實現在記憶體內或接近記憶體的計算。數十年來，處理在記憶體內或接近記憶體的計算一直吸引著越來越多的關注，因為它有潛力突破記憶體壁壘。接近記憶體的計算將計算邏輯移至記憶體附近，從而減少數據移動。最近的研究還表明，某些記憶體可以通過利用記憶體單元的物理特性，將自身轉變為計算單元，實現在記憶體陣列中的原地計算。儘管在記憶體內或接近記憶體的計算可以避免與數據移動相關的開銷，但代價是數據表示和計算的靈活性受到限制，計算能力記憶體的設計挑戰，以及系統和軟件集成的困難。因此，要實現對這些設備的數據密集型應用的有效映射，而不會牺牲準確性或過度增加硬件成本，就需要能夠實現的技術。本書描述了適用於在記憶體內或接近記憶體計算的各種記憶體基板，設計高效可靠的計算設備的架構方法，以及不同類型應用的在記憶體內或接近記憶體加速的機會。

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

Daichi Fujiki在2016年獲得了日本東京的慶應義塾大學的學士學位，並在2017年獲得了美國密歇根州安娜堡的密歇根大學的碩士學位。他目前正在密歇根大學攻讀計算機科學和工程的博士學位。他是密歇根大學的Mbits研究小組的成員，該小組開發用於生物信息學工作負載的原地計算內存架構和定制加速硬件。

Xiaowei Wang在2015年獲得了中國北京清華大學的電子信息科學與技術學士學位。他在2017年獲得了美國密歇根州安娜堡的密歇根大學的計算機科學和工程碩士學位，目前正在該校攻讀計算機科學和工程的博士學位。他的導師是Reetuparna Das教授。他的研究興趣包括面向機器學習的特定領域架構、內存計算和硬件/軟件協同設計。

Arun Subramaniyan在2015年獲得了印度比爾拉理工學院（BITS-Pilani）的電氣與電子學學士學位。他目前是密歇根大學的博士生，由Reetuparna Das教授指導。他的博士論文研究重點是為精確健康開發高效算法和定制計算系統。他還對內存計算架構和硬件可靠性感興趣。