PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster

Large-scale brain-like simulation with high performance and low power consumption is one of the most challenging problems in brain-like computing. At present, the implementation of brain-like computing is mainly divided into hardware implementation and software implementation. Dedicated brain-like c...

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Autor principal:	LI Peiqi1, YU Gongjian2, HUA Xia2, LIU Jiahang2, CHAI Zhilei2,3+
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	brain-like computing spiking neural networks nest simulator field programmable gate array (fpga) pynq framework Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/c924bb15306a4acb9a98fc3f760b1cf4
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id	oai:doaj.org-article:c924bb15306a4acb9a98fc3f760b1cf4
record_format	dspace
spelling	oai:doaj.org-article:c924bb15306a4acb9a98fc3f760b1cf42021-11-10T08:13:09ZPEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster10.3778/j.issn.1673-9418.20110471673-9418https://doaj.org/article/c924bb15306a4acb9a98fc3f760b1cf42021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2952.shtmlhttps://doaj.org/toc/1673-9418Large-scale brain-like simulation with high performance and low power consumption is one of the most challenging problems in brain-like computing. At present, the implementation of brain-like computing is mainly divided into hardware implementation and software implementation. Dedicated brain-like computing chips and systems implemented by hardware can provide better energy efficiency indicators, but they are costly and poorly adaptable. Software-based simulation (such as NEST) has good availability but has the problem of slow computing speed. If the two implementation methods are combined, through the software and hardware co-design, to ensure a good application ecology while obtaining higher computing energy efficiency, this paper proposes a high-energy-efficiency implemen-tation (PEST) of the NEST brain-like simulator based on the FPGA heterogeneous platform PYNQ cluster. By building a large-scale PYNQ cluster, it designs software and hardware data interaction interfaces to realize a scalable brain-like computing system based on the NEST simulator, designs FPGA hardware circuits for IAF neurons, and uses MPI distributed computing to improve NEST computing efficiency. The experimental results show that, for different computing models, under the optimal adaptation of PYNQ cluster, the performance of the neuron update part on PEST is improved by more than 4.6 times compared with AMD 3600X, and by more than 7.5 times compared with Xeon 2620. PEST's updated energy efficiency is more than 5.3 times higher than that of 3600X and 7.9 times higher than that of Xeon 2620.LI Peiqi1, YU Gongjian2, HUA Xia2, LIU Jiahang2, CHAI Zhilei2,3+Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticlebrain-like computingspiking neural networksnest simulatorfield programmable gate array (fpga)pynq frameworkElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2127-2141 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	brain-like computing spiking neural networks nest simulator field programmable gate array (fpga) pynq framework Electronic computers. Computer science QA75.5-76.95
spellingShingle	brain-like computing spiking neural networks nest simulator field programmable gate array (fpga) pynq framework Electronic computers. Computer science QA75.5-76.95 LI Peiqi1, YU Gongjian2, HUA Xia2, LIU Jiahang2, CHAI Zhilei2,3+ PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster
description	Large-scale brain-like simulation with high performance and low power consumption is one of the most challenging problems in brain-like computing. At present, the implementation of brain-like computing is mainly divided into hardware implementation and software implementation. Dedicated brain-like computing chips and systems implemented by hardware can provide better energy efficiency indicators, but they are costly and poorly adaptable. Software-based simulation (such as NEST) has good availability but has the problem of slow computing speed. If the two implementation methods are combined, through the software and hardware co-design, to ensure a good application ecology while obtaining higher computing energy efficiency, this paper proposes a high-energy-efficiency implemen-tation (PEST) of the NEST brain-like simulator based on the FPGA heterogeneous platform PYNQ cluster. By building a large-scale PYNQ cluster, it designs software and hardware data interaction interfaces to realize a scalable brain-like computing system based on the NEST simulator, designs FPGA hardware circuits for IAF neurons, and uses MPI distributed computing to improve NEST computing efficiency. The experimental results show that, for different computing models, under the optimal adaptation of PYNQ cluster, the performance of the neuron update part on PEST is improved by more than 4.6 times compared with AMD 3600X, and by more than 7.5 times compared with Xeon 2620. PEST's updated energy efficiency is more than 5.3 times higher than that of 3600X and 7.9 times higher than that of Xeon 2620.
format	article
author	LI Peiqi1, YU Gongjian2, HUA Xia2, LIU Jiahang2, CHAI Zhilei2,3+
author_facet	LI Peiqi1, YU Gongjian2, HUA Xia2, LIU Jiahang2, CHAI Zhilei2,3+
author_sort	LI Peiqi1, YU Gongjian2, HUA Xia2, LIU Jiahang2, CHAI Zhilei2,3+
title	PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster
title_short	PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster
title_full	PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster
title_fullStr	PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster
title_full_unstemmed	PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster
title_sort	pest: energy-efficient nest brain-like simulator implemented by pynq cluster
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/c924bb15306a4acb9a98fc3f760b1cf4
work_keys_str_mv	AT lipeiqi1yugongjian2huaxia2liujiahang2chaizhilei23 pestenergyefficientnestbrainlikesimulatorimplementedbypynqcluster
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PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster

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