research-article

SNEAP: A Fast and Efficient Toolchain for Mapping Large-Scale Spiking Neural Network onto NoC-based Neuromorphic Platform

Authors:

Weixia XuAuthors Info & Claims

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

Pages 9 - 14

https://doi.org/10.1145/3386263.3406900

Published: 07 September 2020 Publication History

Abstract

Spiking neural network (SNN), as the third generation of artificial neural networks, has been widely adopted in vision and audio tasks. Nowadays, many neuromorphic platforms support SNN simulation and adopt Network-on-Chips (NoC) architecture for multi-cores interconnection. However, a large volume and run-time communication on the interconnection has a significant effect on performance of the platform. In this paper, we propose a toolchain called SNEAP (Spiking NEural network mAPping toolchain) for mapping SNNs to neuromorphic platforms with multi-cores, which aims to reduce the energy and latency brought by spike communication on the interconnection.

SNEAP includes two key steps: partitioning the SNN to reduce the spikes communicated between partitions, and mapping the partitions of SNN to the NoC to reduce average hop of spikes under the constraint of hardware resources. SNEAP effectively reduces the energy and latency on the NoC-based neuromorphic platform and spend less time than other toolchains.

The experimental results show that SNEAP can achieve average 418X reduction in end-to-end execution time, and reduce energy consumption and spike latency, on average, by 23% and 51% respectively, compared with SpiNeMap.

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Kang ZZhu JXiao XLi SWang LMa DPan G(2024)LSM-Based Hotspot Prediction and Hotspot-Aware Routing in NoC-Based Neuromorphic ProcessorIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.337085032:7(1239-1252)Online publication date: Jul-2024
https://doi.org/10.1109/TVLSI.2024.3370850
Xiao CHe XYang ZXiao XWang YGong RTie JWang LXu W(2024)Hierarchical Mapping of Large-Scale Spiking Convolutional Neural Networks Onto Resource-Constrained Neuromorphic ProcessorIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.334407043:5(1442-1455)Online publication date: May-2024
https://doi.org/10.1109/TCAD.2023.3344070
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Index Terms

SNEAP: A Fast and Efficient Toolchain for Mapping Large-Scale Spiking Neural Network onto NoC-based Neuromorphic Platform
1. Hardware

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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.

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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New York, NY, United States

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Published: 07 September 2020

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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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Cited By

Kang ZZhu JXiao XLi SWang LMa DPan G(2024)LSM-Based Hotspot Prediction and Hotspot-Aware Routing in NoC-Based Neuromorphic ProcessorIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.337085032:7(1239-1252)Online publication date: Jul-2024
https://doi.org/10.1109/TVLSI.2024.3370850
Xiao CHe XYang ZXiao XWang YGong RTie JWang LXu W(2024)Hierarchical Mapping of Large-Scale Spiking Convolutional Neural Networks Onto Resource-Constrained Neuromorphic ProcessorIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.334407043:5(1442-1455)Online publication date: May-2024
https://doi.org/10.1109/TCAD.2023.3344070
Xue JXie LChen FWu LTian QZhou YYing RLiu P(2023)EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge ComputingSensors10.3390/s2314654823:14(6548)Online publication date: 20-Jul-2023
https://doi.org/10.3390/s23146548
Weber JWu CPayvand MPayvand MParsa MSchuman C(2023)GMap : An Open-source Efficient Compiler for Mapping any Network onto any Neuromophic ChipProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3605997(1-4)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3605997
Yang ZWang LShi WWang YTie JWang FYu XPeng LXiao CXiao XYao YZhou GYu XGong RZhao XTang YXu W(2023)Back to Homogeneous Computing: A Tightly-Coupled Neuromorphic Processor With Neuromorphic ISAIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.330740834:11(2910-2927)Online publication date: Nov-2023
https://doi.org/10.1109/TPDS.2023.3307408
He JShen ZTian FChen JYang JSawan MCheng TBogdan PTsui C(2023)SNNOpt: An Application-Specific Design Framework for Spiking Neural Networks2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)10.1109/AICAS57966.2023.10168605(1-5)Online publication date: 11-Jun-2023
https://doi.org/10.1109/AICAS57966.2023.10168605
Dang KDoan NNguyen NAbdallah A(2023)HeterGenMap: An Evolutionary Mapping Framework for Heterogeneous NoC-Based Neuromorphic SystemsIEEE Access10.1109/ACCESS.2023.334516811(144095-144112)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3345168
Kang ZLi SWang SQu LGong RShi WXu WWang L(2023)Path-Based Multicast Routing for Network-on-Chip of the Neuromorphic ProcessorJournal of Computer Science and Technology10.1007/s11390-022-1232-838:5(1098-1112)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s11390-022-1232-8
Xiao CWang YChen JWang L(2022)Topology-Aware Mapping of Spiking Neural Network to Neuromorphic ProcessorElectronics10.3390/electronics1118286711:18(2867)Online publication date: 10-Sep-2022
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Mysore NHota GDeiss SPedroni BCauwenberghs G(2022)Hierarchical Network Connectivity and Partitioning for Reconfigurable Large-Scale Neuromorphic SystemsFrontiers in Neuroscience10.3389/fnins.2021.79765415Online publication date: 31-Jan-2022
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