Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85628
Title: Distributed In-Memory Computing on Binary RRAM Crossbar
Authors: Ni, Leibin
Huang, Hantao
Liu, Zichuan
Joshi, Rajiv V.
Yu, Hao
Keywords: RRAM crossbar
Hardware accelerator
Issue Date: 2017
Source: Ni, L., Huang, H., Liu, Z., Joshi, R. V., & Yu, H. (2017). Distributed In-Memory Computing on Binary RRAM Crossbar. ACM Journal on Emerging Technologies in Computing Systems, 13(3), 36-.
Series/Report no.: ACM Journal on Emerging Technologies in Computing Systems
Abstract: The recently emerging resistive random-access memory (RRAM) can provide nonvolatile memory storage but also intrinsic computing for matrix-vector multiplication, which is ideal for the low-power and high-throughput data analytics accelerator performed in memory. However, the existing RRAM crossbar--based computing is mainly assumed as a multilevel analog computing, whose result is sensitive to process nonuniformity as well as additional overhead from AD-conversion and I/O. In this article, we explore the matrix-vector multiplication accelerator on a binary RRAM crossbar with adaptive 1-bit-comparator--based parallel conversion. Moreover, a distributed in-memory computing architecture is also developed with the according control protocol. Both memory array and logic accelerator are implemented on the binary RRAM crossbar, where the logic-memory pair can be distributed with the control bus protocol. Experimental results have shown that compared to the analog RRAM crossbar, the proposed binary RRAM crossbar can achieve significant area savings with better calculation accuracy. Moreover, significant speedup can be achieved for matrix-vector multiplication in neural network--based machine learning such that the overall training and testing time can be both reduced. In addition, large energy savings can be also achieved when compared to the traditional CMOS-based out-of-memory computing architecture.
URI: https://hdl.handle.net/10356/85628
http://hdl.handle.net/10220/43796
ISSN: 1550-4832
DOI: 10.1145/2996192
Rights: © 2017 Association for Computing Machinery (ACM). This is the author created version of a work that has been peer reviewed and accepted for publication by ACM Journal on Emerging Technologies in Computing Systems, Association for Computing Machinery (ACM). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2996192].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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