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research-article

Memristive Crossbar Mapping for Neuromorphic Computing Systems on 3D IC

Authors:

Feng WuAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 25, Issue 1

Article No.: 8, Pages 1 - 19

https://doi.org/10.1145/3365576

Published: 25 November 2019 Publication History

Abstract

In recent years, neuromorphic computing systems based on memristive crossbar have provided a promising solution to enable acceleration of neural networks. However, most of the neural networks used in realistic applications are often sparse. If such sparse neural network is directly implemented on a single memristive crossbar, then it would result in inefficient hardware realizations. In this work, we propose E3D-FNC, an enhanced three-dimesnional (3D) floorplanning framework for neuromorphic computing systems, in which the neuron clustering and the layer assignment are considered interactively. First, in each iteration, hierarchical clustering partitions neurons into a set of clusters under the guidance of the proposed distance metric. The optimal number of clusters is determined by L-method. Then matrix re-ordering is proposed to re-arrange the columns of the weight matrix in each cluster. As a result, the reordered connection matrix can be easily mapped into a set of crossbars with high utilizations. Next, since the clustering results will in turn affect the floorplan, we perform the floorplanning of neurons and crossbars again. All the proposed methodologies are embedded in an iterative framework to improve the quality of NCS design. Finally, a 3D floorplan of neuromorphic computing systems is generated. Experimental results show that E3D-FNC can achieve highly hardware-efficient designs compared to the state of the art.

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Cited By

Xu QWang JYuan BSun QChen SYu BKang YWu F(2023)Reliability-Driven Memristive Crossbar Design in Neuromorphic Computing SystemsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.312506520:1(74-87)Online publication date: Jan-2023
https://doi.org/10.1109/TASE.2021.3125065
Mukhopadhyay ANaligala MDuggisetty DChakrabarti ISharad M(2022)Acoustic scene analysis using analog spiking neural networkNeuromorphic Computing and Engineering10.1088/2634-4386/ac90e52:4(044003)Online publication date: 11-Oct-2022
https://doi.org/10.1088/2634-4386/ac90e5
Xu QWang JGeng HChen SWen X(2021)Reliability-Driven Neuromorphic Computing Systems Design2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE51398.2021.9473929(1586-1591)Online publication date: 1-Feb-2021
https://doi.org/10.23919/DATE51398.2021.9473929
Show More Cited By

Index Terms

Memristive Crossbar Mapping for Neuromorphic Computing Systems on 3D IC
1. Hardware

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Information & Contributors

Information

Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 25, Issue 1

January 2020

299 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3370083

Editor:
Naehyuck Chang
Korea Advanced Institute of Science and Technology, Korea

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 25 November 2019

Accepted: 01 September 2019

Revised: 01 May 2019

Received: 01 January 2019

Published in TODAES Volume 25, Issue 1

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National Natural Science Foundation of China
The Research Grants Council of Hong Kong SAR
Beijng Municipal Science & Technology Program

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Cited By

Xu QWang JYuan BSun QChen SYu BKang YWu F(2023)Reliability-Driven Memristive Crossbar Design in Neuromorphic Computing SystemsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.312506520:1(74-87)Online publication date: Jan-2023
https://doi.org/10.1109/TASE.2021.3125065
Mukhopadhyay ANaligala MDuggisetty DChakrabarti ISharad M(2022)Acoustic scene analysis using analog spiking neural networkNeuromorphic Computing and Engineering10.1088/2634-4386/ac90e52:4(044003)Online publication date: 11-Oct-2022
https://doi.org/10.1088/2634-4386/ac90e5
Xu QWang JGeng HChen SWen X(2021)Reliability-Driven Neuromorphic Computing Systems Design2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE51398.2021.9473929(1586-1591)Online publication date: 1-Feb-2021
https://doi.org/10.23919/DATE51398.2021.9473929
Kaibartta TBiswas GPal ADas D(2021)A Genetic Algorithm-Based Metaheuristic Approach for Test Cost Optimization of 3D SICIEEE Access10.1109/ACCESS.2021.31313369(160987-161002)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3131336

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