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Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI

Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for power-constrained environments where sensors and edge...

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Autores principales: Xiao, Chao, Chen, Jihua, Wang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572825/
https://www.ncbi.nlm.nih.gov/pubmed/36236344
http://dx.doi.org/10.3390/s22197248
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author Xiao, Chao
Chen, Jihua
Wang, Lei
author_facet Xiao, Chao
Chen, Jihua
Wang, Lei
author_sort Xiao, Chao
collection PubMed
description Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for power-constrained environments where sensors and edge nodes of the internet of things (IoT) work. The mapping of SNNs onto neuromorphic hardware is challenging because a non-optimized mapping may result in a high network-on-chip (NoC) latency and energy consumption. In this paper, we propose NeuMap, a simple and fast toolchain, to map SNNs onto the multicore neuromorphic hardware. NeuMap first obtains the communication patterns of an SNN by calculation that simplifies the mapping process. Then, NeuMap exploits localized connections, divides the adjacent layers into a sub-network, and partitions each sub-network into multiple clusters while meeting the hardware resource constraints. Finally, we employ a meta-heuristics algorithm to search for the best cluster-to-core mapping scheme in the reduced searching space. We conduct experiments using six realistic SNN-based applications to evaluate NeuMap and two prior works (SpiNeMap and SNEAP). The experimental results show that, compared to SpiNeMap and SNEAP, NeuMap reduces the average energy consumption by 84% and 17% and has 55% and 12% lower spike latency, respectively.
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spelling pubmed-95728252022-10-17 Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI Xiao, Chao Chen, Jihua Wang, Lei Sensors (Basel) Article Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for power-constrained environments where sensors and edge nodes of the internet of things (IoT) work. The mapping of SNNs onto neuromorphic hardware is challenging because a non-optimized mapping may result in a high network-on-chip (NoC) latency and energy consumption. In this paper, we propose NeuMap, a simple and fast toolchain, to map SNNs onto the multicore neuromorphic hardware. NeuMap first obtains the communication patterns of an SNN by calculation that simplifies the mapping process. Then, NeuMap exploits localized connections, divides the adjacent layers into a sub-network, and partitions each sub-network into multiple clusters while meeting the hardware resource constraints. Finally, we employ a meta-heuristics algorithm to search for the best cluster-to-core mapping scheme in the reduced searching space. We conduct experiments using six realistic SNN-based applications to evaluate NeuMap and two prior works (SpiNeMap and SNEAP). The experimental results show that, compared to SpiNeMap and SNEAP, NeuMap reduces the average energy consumption by 84% and 17% and has 55% and 12% lower spike latency, respectively. MDPI 2022-09-24 /pmc/articles/PMC9572825/ /pubmed/36236344 http://dx.doi.org/10.3390/s22197248 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xiao, Chao
Chen, Jihua
Wang, Lei
Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
title Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
title_full Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
title_fullStr Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
title_full_unstemmed Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
title_short Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
title_sort optimal mapping of spiking neural network to neuromorphic hardware for edge-ai
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572825/
https://www.ncbi.nlm.nih.gov/pubmed/36236344
http://dx.doi.org/10.3390/s22197248
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