Review on FPGA-Based Accelerators in Deep Learning

For the past few years, with rapid development of Internet and big data, artificial intelligence has become popular, and it is the rise of deep learning that promotes the rapid development of AI. The problem that needs to be solved urgently in the era of big data is how to effectively analyze and u...

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Autor principal:	LIU Tengda1, ZHU Junwen1, ZHANG Yiwen2+
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	deep learning; neural networks field programmable gate array (fpga) hardware accelerator Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/76e948fca2fc46b29ce8fef02523a493
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id	oai:doaj.org-article:76e948fca2fc46b29ce8fef02523a493
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spelling	oai:doaj.org-article:76e948fca2fc46b29ce8fef02523a4932021-11-10T08:07:17ZReview on FPGA-Based Accelerators in Deep Learning10.3778/j.issn.1673-9418.21040121673-9418https://doaj.org/article/76e948fca2fc46b29ce8fef02523a4932021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2949.shtmlhttps://doaj.org/toc/1673-9418For the past few years, with rapid development of Internet and big data, artificial intelligence has become popular, and it is the rise of deep learning that promotes the rapid development of AI. The problem that needs to be solved urgently in the era of big data is how to effectively analyze and use extremely complex and diverse data, and then make full use of the value of data and benefit mankind. As a technology of machine learning, deep learning which has been widely used in speech recognition, image recognition, natural language processing and many other fields is an important magic weapon to solve this problem. It plays an increasingly important role in data processing and changes traditional machine learning methods. How to effectively accelerate the computing power of deep learning has always been the focus of scientific research. With strong parallel computing power and low power consumption, FPGA has become a strong competitor of GPU in the field of deep learning acceleration. Starting from the typical models of deep learning, on the basis of the existing characteristics of FPGA acceleration technology, the research status of various accelerators is summarized from four aspects: accelerators for neural network models, accelerators for a specific application, accelerators for optimization strategies, and general accelerator frameworks with hardware templates. Then, the performance of different acceleration technologies in different models is compared. Finally, the possible development direction in the future is prospected.LIU Tengda1, ZHU Junwen1, ZHANG Yiwen2+Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticledeep learning; neural networksfield programmable gate array (fpga)hardware acceleratorElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2093-2104 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	deep learning; neural networks field programmable gate array (fpga) hardware accelerator Electronic computers. Computer science QA75.5-76.95
spellingShingle	deep learning; neural networks field programmable gate array (fpga) hardware accelerator Electronic computers. Computer science QA75.5-76.95 LIU Tengda1, ZHU Junwen1, ZHANG Yiwen2+ Review on FPGA-Based Accelerators in Deep Learning
description	For the past few years, with rapid development of Internet and big data, artificial intelligence has become popular, and it is the rise of deep learning that promotes the rapid development of AI. The problem that needs to be solved urgently in the era of big data is how to effectively analyze and use extremely complex and diverse data, and then make full use of the value of data and benefit mankind. As a technology of machine learning, deep learning which has been widely used in speech recognition, image recognition, natural language processing and many other fields is an important magic weapon to solve this problem. It plays an increasingly important role in data processing and changes traditional machine learning methods. How to effectively accelerate the computing power of deep learning has always been the focus of scientific research. With strong parallel computing power and low power consumption, FPGA has become a strong competitor of GPU in the field of deep learning acceleration. Starting from the typical models of deep learning, on the basis of the existing characteristics of FPGA acceleration technology, the research status of various accelerators is summarized from four aspects: accelerators for neural network models, accelerators for a specific application, accelerators for optimization strategies, and general accelerator frameworks with hardware templates. Then, the performance of different acceleration technologies in different models is compared. Finally, the possible development direction in the future is prospected.
format	article
author	LIU Tengda1, ZHU Junwen1, ZHANG Yiwen2+
author_facet	LIU Tengda1, ZHU Junwen1, ZHANG Yiwen2+
author_sort	LIU Tengda1, ZHU Junwen1, ZHANG Yiwen2+
title	Review on FPGA-Based Accelerators in Deep Learning
title_short	Review on FPGA-Based Accelerators in Deep Learning
title_full	Review on FPGA-Based Accelerators in Deep Learning
title_fullStr	Review on FPGA-Based Accelerators in Deep Learning
title_full_unstemmed	Review on FPGA-Based Accelerators in Deep Learning
title_sort	review on fpga-based accelerators in deep learning
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/76e948fca2fc46b29ce8fef02523a493
work_keys_str_mv	AT liutengda1zhujunwen1zhangyiwen2 reviewonfpgabasedacceleratorsindeeplearning
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Review on FPGA-Based Accelerators in Deep Learning

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