Survey of FPGA based recurrent neural network accelerator

Recurrent neural network(RNN) has been used wildly used in machine learning field in recent years,especially in dealing with sequential learning tasks compared with other neural network like CNN.However,RNN and its variants,such as LSTM,GRU and other fully connected networks,have high computational...

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Main Authors: Chen GAO, Fan ZHANG
Format: Article
Language:English
Published: POSTS&TELECOM PRESS Co., LTD 2019-08-01
Series:网络与信息安全学报
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Online Access:http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2019034
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author Chen GAO
Fan ZHANG
author_facet Chen GAO
Fan ZHANG
author_sort Chen GAO
collection DOAJ
description Recurrent neural network(RNN) has been used wildly used in machine learning field in recent years,especially in dealing with sequential learning tasks compared with other neural network like CNN.However,RNN and its variants,such as LSTM,GRU and other fully connected networks,have high computational and storage complexity,which makes its inference calculation slow and difficult to be applied in products.On the one hand,traditional computing platforms such as CPU are not suitable for large-scale matrix operation of RNN.On the other hand,the shared memory and global memory of hardware acceleration platform GPU make the power consumption of GPU-based RNN accelerator higher.More and more research has been done on the RNN accelerator of the FPGA in recent years because of its parallel computing and low power consumption performance.An overview of the researches on RNN accelerator based on FPGA in recent years is given.The optimization algorithm of software level and the architecture design of hardware level used in these accelerator are summarized and some future research directions are proposed.
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institution Kabale University
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series 网络与信息安全学报
spelling doaj-art-c91e5ba033de46798cdde1d524f65b702025-01-15T03:13:36ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2019-08-01511359556283Survey of FPGA based recurrent neural network acceleratorChen GAOFan ZHANGRecurrent neural network(RNN) has been used wildly used in machine learning field in recent years,especially in dealing with sequential learning tasks compared with other neural network like CNN.However,RNN and its variants,such as LSTM,GRU and other fully connected networks,have high computational and storage complexity,which makes its inference calculation slow and difficult to be applied in products.On the one hand,traditional computing platforms such as CPU are not suitable for large-scale matrix operation of RNN.On the other hand,the shared memory and global memory of hardware acceleration platform GPU make the power consumption of GPU-based RNN accelerator higher.More and more research has been done on the RNN accelerator of the FPGA in recent years because of its parallel computing and low power consumption performance.An overview of the researches on RNN accelerator based on FPGA in recent years is given.The optimization algorithm of software level and the architecture design of hardware level used in these accelerator are summarized and some future research directions are proposed.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2019034recurrent neural networkFPGAaccelerator
spellingShingle Chen GAO
Fan ZHANG
Survey of FPGA based recurrent neural network accelerator
网络与信息安全学报
recurrent neural network
FPGA
accelerator
title Survey of FPGA based recurrent neural network accelerator
title_full Survey of FPGA based recurrent neural network accelerator
title_fullStr Survey of FPGA based recurrent neural network accelerator
title_full_unstemmed Survey of FPGA based recurrent neural network accelerator
title_short Survey of FPGA based recurrent neural network accelerator
title_sort survey of fpga based recurrent neural network accelerator
topic recurrent neural network
FPGA
accelerator
url http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2019034
work_keys_str_mv AT chengao surveyoffpgabasedrecurrentneuralnetworkaccelerator
AT fanzhang surveyoffpgabasedrecurrentneuralnetworkaccelerator