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ReRAM-Based Processing-in-Memory (PIM)

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Processing-in-Memory for AI

Abstract

Emerging applications such as artificial intelligence and machine learning have created interest in hardware accelerators for processing parallel data considerably in various neural networks. One of the most critical arithmetic functions in neural networks is Multiply-and-Accumulate (MAC). In the conventional computing architecture (i.e., Von Neumann architecture), processing elements and memory are separated. MAC operations require massive data transfer between processing elements and memory, which consumes a huge amount of power. Recently, processing-in-memory (PIM) architectures have been introduced to address the bottleneck of Von Neumann architecture [1–10]. Since PIM architectures include local computing circuits and memory, we can minimize the data transfer from/to external memory. In general, it is well known that the PIM architectures can improve energy efficiency by orders of magnitude. While SRAM and DRAM are commonly considered in PIM architectures, ReRAM has also gained increasing interest because of the accelerator development for edge computing [11–17]. Many edge devices such as wearables, IoT devices, and biomedical devices require high energy efficiency with compromised performance. Particularly, the edge devices process data scarcely and mostly stay in the standby condition. Since ReRAM offers moderate performance and non-volatility, ReRAM-based PIM architectures can be promising solutions for edge computing. However, ReRAM technologies are not mature yet, and various design issues for ReRAM-based PIM should be tackled comprehensively. In this chapter, we will introduce various ReRAM-based PIM designs covering from unit cells, circuit techniques, and architectures. Besides MAC, other essential logic-in-memory functions will also be discussed.

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Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Tony Tae-Hyoung Kim, Lu Lu & Yuzong Chen

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  1. Tony Tae-Hyoung Kim
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  2. Lu Lu
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  3. Yuzong Chen
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Correspondence to Tony Tae-Hyoung Kim .

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Editors and Affiliations

  1. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Joo-Young Kim

  2. University of California, Santa Barbara, CA, USA

    Bongjin Kim

  3. Nanyang Technological University, Nanyang, Singapore

    Tony Tae-Hyoung Kim

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Kim, T.TH., Lu, L., Chen, Y. (2023). ReRAM-Based Processing-in-Memory (PIM). In: Kim, JY., Kim, B., Kim, T.TH. (eds) Processing-in-Memory for AI. Springer, Cham. https://doi.org/10.1007/978-3-030-98781-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-98781-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98780-0

  • Online ISBN: 978-3-030-98781-7

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