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Application-specific network-on-chip design space exploration framework for neuromorphic processor

Authors:

Weixia XuAuthors Info & Claims

CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers

Pages 71 - 80

https://doi.org/10.1145/3387902.3392626

Published: 23 May 2020 Publication History

Abstract

Neuromorphic processors can support the design of various Spiking Neural Networks (SNN) to deal with different tasks, such as recognition and tracking. Neuromorphic processors use Network-on-Chip (NoC) to support communication between neurons in SNN. The different SNN has different communication traffic patterns. It will pose the different challenges of the NoC designing. A reasonable NoC architecture can improve the overall performance such as lower latency of the processor. Hence, it is critical to implement the exploration of NoC architecture design for neuromorphic processors.

This paper proposes a rapid NoC design space exploration (DSE) framework. As to our knowledge, it is the first work for the NoC DSE for the neuromorphic processor. The framework takes the spikes of the SNN application as input. It can support multiple optimization objectives for NoC design. Meanwhile, an optimized simulated annealing algorithm has been used to perform the DSE for the NoC design space. Then it outputs the final NoC design configuration. We apply this framework to 7 SNN applications to perform the NoC DSE. Compared with baseline NoC configuration, the NoC DSE framework can improve performance (Average Transport latency) by 54% to 93%. Compared with the Simulated Annealing (SA) algorithm, the Better-History SA (BHSA) algorithm speeds up the searching process by 1.5 to 8 times.

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Cited By

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
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
Show More Cited By

Index Terms

Application-specific network-on-chip design space exploration framework for neuromorphic processor
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Networking hardware
  2. Emerging technologies
    1. Biology-related information processing
      1. Neural systems

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Published In

cover image ACM Conferences

CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers

May 2020

298 pages

ISBN:9781450379564

DOI:10.1145/3387902

General Chairs:
Maurizio Palesi
University of Catania, IT
,
Gianluca Palermo
Politecnico di Milano, IT
,
Program Chairs:
Cat Graves
Hewlett Packard Labs
,
Eishi Arima
ITC University of Tokyo, JP

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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Published: 23 May 2020

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National Key RD Program of China
National Natural Science Foundation of China
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CF '20: Computing Frontiers Conference

May 11 - 13, 2020

Sicily, Catania, Italy

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Overall Acceptance Rate 273 of 785 submissions, 35%

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22nd ACM International Conference on Computing Frontiers

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Cited By

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
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
Eissa SStuijk SCorporaal H(2022)DNAsim: Evaluation Framework for Digital Neuromorphic Architectures2022 25th Euromicro Conference on Digital System Design (DSD)10.1109/DSD57027.2022.00065(438-445)Online publication date: Aug-2022
https://doi.org/10.1109/DSD57027.2022.00065

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