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Title: Reconfigurable 2T2R ReRAM architecture for versatile data storage and computing in-memory
Authors: Chen, Yuzong
Lu, Lu
Kim, Bongjin
Kim, Tony Tae-Hyoung
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Chen, Y., Lu, L., Kim, B. & Kim, T. T. (2020). Reconfigurable 2T2R ReRAM architecture for versatile data storage and computing in-memory. IEEE Transactions On Very Large Scale Integration (VLSI) Systems, 28(12), 2636-2649.
Project: I1801E0030 
Journal: IEEE Transactions on Very Large Scale Integration (VLSI) Systems 
Abstract: Nonvolatile memory (NVM)-based computing in-memory (CIM) is a promising solution to data-intensive applications. This work proposes a 2T2R resistive random access memory (ReRAM) architecture that supports three types of CIM operations: 1) ternary content addressable memory (TCAM); 2) logic in-memory (LiM) primitives and arithmetic blocks such as full adder (FA) and full subtractor; and 3) in-memory dot-product for neural networks. The proposed architecture allows the NVM operations in both 2T2R and conventional 1T1R configurations. The proposed LiM full adder (LiM-FA) improves the delay, the static power, and the dynamic power by 3.2×, 1.2×, and 1.6×, respectively, compared with state-of-the-art LiM-FAs. Furthermore, based on different optimization techniques and robustness analysis, a lower precharge voltage is set for each mode. This reduces the TCAM search energy and 1T1R ReRAM access energy by 1.6× and 1.14×, respectively, compared with the case without optimizations.
ISSN: 1063-8210
DOI: 10.1109/TVLSI.2020.3028848
Schools: School of Electrical and Electronic Engineering 
Research Centres: Centre for Integrated Circuits and Systems 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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