research-article

NCPower: Power Modelling for NVM-based Neuromorphic Chip

Authors:

Huaipeng Zhang,

Paramasivam Vishnu,

Rick Siow Mong GohAuthors Info & Claims

ICONS 2020: International Conference on Neuromorphic Systems 2020

Article No.: 15, Pages 1 - 7

https://doi.org/10.1145/3407197.3407619

Published: 28 July 2020 Publication History

Abstract

Spiking neural networks (SNN) on non-volatile memory (NVM) based neuromorphic computing (NC) chips have been regarded as a promising solution in power constrained scenarios, such as Internet of Things (IoT), due to its low energy consumption. The high power efficiency of NC is due to various aspects including the non-von Neumann architecture of NC chip, low power NVM, and the event driven computation of SNN etc., and introduces a large space for low power design exploration. Therefore, a comprehensive quantitative study of the power modelling for such neuromorphic computing system is important for low power design. In this work, we propose NCPower, an energy consumption estimator for NVM-based neuromorphic chip. We systemically developed analytical models based on physical laws, and verify them by comparing the analytical results with measurement results from different neuromorphic chips. We integrated NCPower in a simulator, and analyzed the accuracy and energy consumption of both the traditional multi-spike based SNN and the new single-spike based SNN. It shows that the single-spike model has 7X energy efficiency over the multi-spike model, with similar accuracy under the CIFAR-10 dataset.

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Cited By

Wang ZGu XGoh RZhou JLuo T(2024)Efficient Spiking Neural Networks With Radix EncodingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.319591835:3(3689-3701)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3195918
Zhang HHo NPolat DChen PWahib MNguyen TMeng JGoh RMatsuoka SLuo TWong W(2023)Simeuro: A Hybrid CPU-GPU Parallel Simulator for Neuromorphic Computing ChipsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.329179534:10(2767-2782)Online publication date: Oct-2023
https://doi.org/10.1109/TPDS.2023.3291795
Song SBalaji ADas AKandasamy N(2022)Design-Technology Co-Optimization for NVM-Based Neuromorphic Processing ElementsACM Transactions on Embedded Computing Systems10.1145/352406821:6(1-27)Online publication date: 12-Dec-2022
https://dl.acm.org/doi/10.1145/3524068

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cover image ACM Other conferences

ICONS 2020: International Conference on Neuromorphic Systems 2020

July 2020

186 pages

ISBN:9781450388511

DOI:10.1145/3407197

Copyright © 2020 ACM.

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Publication History

Published: 28 July 2020

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Singapore Government?s Research, Innovation and Enterprise 2020 Plan (Advanced Manufacturing and Engineering domain)

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ICONS 2020

ICONS 2020: International Conference on Neuromorphic Systems 2020

July 28 - 30, 2020

TN, Oak Ridge, USA

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Overall Acceptance Rate 13 of 22 submissions, 59%

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Cited By

Wang ZGu XGoh RZhou JLuo T(2024)Efficient Spiking Neural Networks With Radix EncodingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.319591835:3(3689-3701)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3195918
Zhang HHo NPolat DChen PWahib MNguyen TMeng JGoh RMatsuoka SLuo TWong W(2023)Simeuro: A Hybrid CPU-GPU Parallel Simulator for Neuromorphic Computing ChipsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.329179534:10(2767-2782)Online publication date: Oct-2023
https://doi.org/10.1109/TPDS.2023.3291795
Song SBalaji ADas AKandasamy N(2022)Design-Technology Co-Optimization for NVM-Based Neuromorphic Processing ElementsACM Transactions on Embedded Computing Systems10.1145/352406821:6(1-27)Online publication date: 12-Dec-2022
https://dl.acm.org/doi/10.1145/3524068

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