research-article

MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems

Authors:

Gokul Krishnan,

Huazhong YangAuthors Info & Claims

GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI

Pages 83 - 88

https://doi.org/10.1145/3386263.3407647

Published: 07 September 2020 Publication History

Abstract

Memristor based neuromorphic computing systems give alternative solutions to boost the computing energy efficiency of Neural Network (NN) algorithms. Because of the large-scale applications and the large architecture design space, many factors will affect the computing accuracy and system's performance. In this work, we propose a behavior-level modeling tool for memristor-based neuromorphic computing systems, MNSIM 2.0, to model the performance and help researchers to realize an early-stage design space exploration. Compared with the former version and other benchmarks, MNSIM 2.0 has the following new features: 1. In the algorithm level, MNSIM 2.0 supports the inference accuracy simulation for mixed-precision NNs considering non-ideal factors. 2. In the architecture level, a hierarchical modeling structure for PIM systems is proposed. Users can customize their designs from the aspects of devices, interfaces, processing units, buffer designs, and interconnections. 3. Two hardware-aware algorithm optimization methods are integrated in MNSIM 2.0 to realize software-hardware co-optimization.

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Yousuf OHoskins BRamu KFream MBorders WMadhavan ADaniels MDienstfrey AMcClelland JLueker-Boden MAdam G(2025)Layer ensemble averaging for fault tolerance in memristive neural networksNature Communications10.1038/s41467-025-56319-616:1Online publication date: 1-Feb-2025
https://doi.org/10.1038/s41467-025-56319-6
Wang XSun XHan YChen X(2024)PIMSIM-NN: An ISA-based Simulation Framework for Processing-in-Memory Accelerators2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546788(1-2)Online publication date: 25-Mar-2024
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Index Terms

MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems
1. Hardware

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Published In

cover image ACM Other conferences

GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI

September 2020

597 pages

ISBN:9781450379441

DOI:10.1145/3386263

General Chairs:
Tinoosh Mohsenin
University of Maryland, Baltimore County, USA
,
Weisheng Zhao
Beihang University, China
,
Program Chairs:
Yiran Chen
Duke University, USA
,
Onur Mutlu
ETH Zurich, Switzerland

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2020

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National Natural Science Foundation of China
Beijing Academy of Artificial Intelligence under Grant
National Key Research and Development Program of China
Beijing National Research Center for Information Science and Technology
Beijing Innovation Center for Future Chips

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GLSVLSI '20

GLSVLSI '20: Great Lakes Symposium on VLSI 2020

September 7 - 9, 2020

Virtual Event, China

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Overall Acceptance Rate 312 of 1,156 submissions, 27%

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Cited By

Nehete HSoni SBollu TRaman BKaushik B(2025)Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM ArchitectureIEEE Open Journal of Nanotechnology10.1109/OJNANO.2024.35242656(16-26)Online publication date: 2025
https://doi.org/10.1109/OJNANO.2024.3524265
Yousuf OHoskins BRamu KFream MBorders WMadhavan ADaniels MDienstfrey AMcClelland JLueker-Boden MAdam G(2025)Layer ensemble averaging for fault tolerance in memristive neural networksNature Communications10.1038/s41467-025-56319-616:1Online publication date: 1-Feb-2025
https://doi.org/10.1038/s41467-025-56319-6
Wang XSun XHan YChen X(2024)PIMSIM-NN: An ISA-based Simulation Framework for Processing-in-Memory Accelerators2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546788(1-2)Online publication date: 25-Mar-2024
https://doi.org/10.23919/DATE58400.2024.10546788
Liu TLu YYu JNien CCheng H(2024)ReTAP: Processing-in-ReRAM Bitap Approximate String Matching Accelerator for Genomic Analysis2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546504(1-2)Online publication date: 25-Mar-2024
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Wu THe SZhu JChen WYang SChen PYin YZhang XSun XChen G(2024)AUTOHET: An Automated Heterogeneous ReRAM-Based Accelerator for DNN InferenceProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673143(1052-1061)Online publication date: 12-Aug-2024
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Park JKim BSung H(2024)NavCim: Comprehensive Design Space Exploration for Analog Computing-in-Memory ArchitecturesProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676946(168-182)Online publication date: 14-Oct-2024
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Wang CDong ZZhou DZhu ZWang YFeng JKeutzer KDe V(2024)EPIM: Efficient Processing-In-Memory Accelerators based on EpitomeProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3657377(1-6)Online publication date: 23-Jun-2024
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Qian YZhao LMeng FXu XZhuo CYin X(2024)Enhancing ConvNets With ConvFIFO: A Crossbar PIM Architecture Based on Kernel-Stationary First-In-First-Out DataflowIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.340964832:9(1640-1651)Online publication date: Sep-2024
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Wu XHanson EWang NZheng QYang XYang HLi SCheng FPande PDoppa JChakrabarty KLi H(2024)Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-Based DNN AcceleratorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.340919343:12(4558-4571)Online publication date: Dec-2024
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