research-article

ReSMA: accelerating approximate string matching using ReRAM-based content addressable memory

Authors:

Chuangyi GuiAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 991 - 996

https://doi.org/10.1145/3489517.3530559

Published: 23 August 2022 Publication History

Abstract

Approximate string matching (ASM) functions as the basic operation kernel for a large number of string processing applications. Existing Von-Neumann-based ASM accelerators suffer from huge intermediate data with the ever-increasing string data, leading to massive off-chip data transmissions. This paper presents a novel ASM processing-in-memory (PIM) accelerator, namely ReSMA, based on ReCAM- and ReRAM-arrays to eliminate the off-chip data transmissions in ASM. We develop a novel ReCAM-friendly filter-and-filtering algorithm to process the q-grams filtering in ReCAM memory. We also design a new data mapping strategy and a new verification algorithm, which enables computing the edit distances totally in ReRAM crossbars for energy saving. Experimental results show that ReSMA outperforms the CPU-, GPU-, FPGA-, ASIC-, and PIM-based solutions by 268.7×, 38.6×, 20.9×, 707.8×, and 14.7× in terms of performance, and 153.8×, 42.2×, 31.6×, 18.3×, and 5.3× in terms of energy-saving, respectively.

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Cited By

Li HChen DMitra T(2025)SADIMM: Accelerating Sparse Attention Using DIMM-Based Near-Memory ProcessingIEEE Transactions on Computers10.1109/TC.2024.350036274:2(542-554)Online publication date: Feb-2025
https://doi.org/10.1109/TC.2024.3500362
Li HLi ZBai ZMitra T(2024)ASADI: Accelerating Sparse Attention Using Diagonal-based In-Situ Computing2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00065(774-787)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00065
Zhong HChen ZHuangfu WWang CXu YWang TYu YLiu YNarayanan VYang HLi X(2023)ASMCap: An Approximate String Matching Accelerator for Genome Sequence Analysis Based on Capacitive Content Addressable Memory2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247937(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247937

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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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Publication History

Published: 23 August 2022

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DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Li HChen DMitra T(2025)SADIMM: Accelerating Sparse Attention Using DIMM-Based Near-Memory ProcessingIEEE Transactions on Computers10.1109/TC.2024.350036274:2(542-554)Online publication date: Feb-2025
https://doi.org/10.1109/TC.2024.3500362
Li HLi ZBai ZMitra T(2024)ASADI: Accelerating Sparse Attention Using Diagonal-based In-Situ Computing2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00065(774-787)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00065
Zhong HChen ZHuangfu WWang CXu YWang TYu YLiu YNarayanan VYang HLi X(2023)ASMCap: An Approximate String Matching Accelerator for Genome Sequence Analysis Based on Capacitive Content Addressable Memory2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247937(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247937

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