In-/Near-Memory Computing
暫譯: 內存/近內存計算

Daichi Fujiki , Xiaowei Wang , Arun Subramaniyan

In-/Near-Memory Computing
  • 出版商: Morgan & Claypool
  • 出版日期: 2021-08-12
  • 售價: $2,710
  • 貴賓價: 9.5 折 $2,575
  • 語言: 英文
  • 頁數: 140
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1636391885
  • ISBN-13: 9781636391885
  • 其他版本: In-/Near-Memory Computing

    • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

這本書提供了有關內存計算和近內存計算的關鍵概念和技術的結構化介紹。數十年來，內存計算（processing-in-memory）或近內存計算（near-memory computing）因其打破內存瓶頸的潛力而受到越來越多的關注。近內存計算將計算邏輯移至內存附近，從而減少數據移動。最近的研究也顯示，某些內存可以利用內存單元的物理特性轉變為計算單元，實現內存陣列中的原位計算（in-situ computing）。雖然內存和近內存計算可以繞過與數據移動相關的開銷，但這也帶來了數據表示和計算的靈活性受限、具備計算能力的內存設計挑戰，以及系統和軟件整合的困難。因此，內存/近內存計算的廣泛部署必須依賴於能夠有效將數據密集型應用映射到這些設備的技術，而不會犧牲準確性或過度增加硬件成本。本書描述了各種適合內存和近內存計算的內存基材、設計高效可靠計算設備的架構方法，以及不同類別應用的內存/近內存加速機會。

作者簡介

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.

作者簡介(中文翻譯)

藤木大地於2016年獲得日本東京慶應義塾大學的工學學士學位，並於2017年獲得美國密西根大學安娜堡分校的工程碩士學位。他目前正在密西根大學安娜堡分校攻讀計算機科學與工程的博士學位。他是密西根大學計算機工程實驗室（CELAB）Mbits研究小組的成員，該小組專注於為生物信息學工作負載開發原位計算記憶體架構和定制加速硬體。


王小偉於2015年獲得中國北京清華大學的電子信息科學與技術工學學士學位。2017年，他在美國密西根大學安娜堡分校獲得計算機科學與工程的碩士學位，並目前正在該校攻讀計算機科學與工程的博士學位。他的指導教授是Reetuparna Das教授。他的研究興趣包括機器學習的領域特定架構、內存計算以及硬體/軟體共同設計。


阿倫·蘇布拉馬尼揚於2015年獲得印度比爾拉科技與科學學院（BITS-Pilani）的電氣與電子工程榮譽學士學位。他目前是密西根大學的博士生，指導教授為Reetuparna Das教授。他的論文研究專注於為精準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體可靠性感興趣。他的工作曾獲得密西根大學的精準健康學者獎、Rackham國際學生獎學金、IEEE Micro Top Picks獎以及CODESCISSS最佳論文獎。

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