A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices

Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile...
Ausführliche Beschreibung

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Autor*in:	Shijie Li [verfasserIn] Xiaolong Shen [verfasserIn] Yong Dou [verfasserIn] Shice Ni [verfasserIn] Jinwei Xu [verfasserIn] Ke Yang [verfasserIn] Qiang Wang [verfasserIn] Xin Niu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: Computational Intelligence and Neuroscience - Hindawi Limited, 2007, (2019)
Übergeordnetes Werk:	year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2019/4328653

Katalog-ID:	DOAJ041166302

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520			\|a Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices.
653		0	\|a Computer applications to medicine. Medical informatics
653		0	\|a Neurosciences. Biological psychiatry. Neuropsychiatry
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700	0		\|a Xin Niu \|e verfasserin \|4 aut
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allfieldsGer	10.1155/2019/4328653 doi (DE-627)DOAJ041166302 (DE-599)DOAJac43dc8e2bbd4ce88a6ce879fb7d7611 DE-627 ger DE-627 rakwb eng R858-859.7 RC321-571 Shijie Li verfasserin aut A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices. Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry Xiaolong Shen verfasserin aut Yong Dou verfasserin aut Shice Ni verfasserin aut Jinwei Xu verfasserin aut Ke Yang verfasserin aut Qiang Wang verfasserin aut Xin Niu verfasserin aut In Computational Intelligence and Neuroscience Hindawi Limited, 2007 (2019) (DE-627)545783720 (DE-600)2388208-6 16875265 nnns year:2019 https://doi.org/10.1155/2019/4328653 kostenfrei https://doaj.org/article/ac43dc8e2bbd4ce88a6ce879fb7d7611 kostenfrei http://dx.doi.org/10.1155/2019/4328653 kostenfrei https://doaj.org/toc/1687-5265 Journal toc kostenfrei https://doaj.org/toc/1687-5273 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsSound	10.1155/2019/4328653 doi (DE-627)DOAJ041166302 (DE-599)DOAJac43dc8e2bbd4ce88a6ce879fb7d7611 DE-627 ger DE-627 rakwb eng R858-859.7 RC321-571 Shijie Li verfasserin aut A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices. Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry Xiaolong Shen verfasserin aut Yong Dou verfasserin aut Shice Ni verfasserin aut Jinwei Xu verfasserin aut Ke Yang verfasserin aut Qiang Wang verfasserin aut Xin Niu verfasserin aut In Computational Intelligence and Neuroscience Hindawi Limited, 2007 (2019) (DE-627)545783720 (DE-600)2388208-6 16875265 nnns year:2019 https://doi.org/10.1155/2019/4328653 kostenfrei https://doaj.org/article/ac43dc8e2bbd4ce88a6ce879fb7d7611 kostenfrei http://dx.doi.org/10.1155/2019/4328653 kostenfrei https://doaj.org/toc/1687-5265 Journal toc kostenfrei https://doaj.org/toc/1687-5273 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
language	English
source	In Computational Intelligence and Neuroscience (2019) year:2019
sourceStr	In Computational Intelligence and Neuroscience (2019) year:2019
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry
isfreeaccess_bool	true
container_title	Computational Intelligence and Neuroscience
authorswithroles_txt_mv	Shijie Li @@aut@@ Xiaolong Shen @@aut@@ Yong Dou @@aut@@ Shice Ni @@aut@@ Jinwei Xu @@aut@@ Ke Yang @@aut@@ Qiang Wang @@aut@@ Xin Niu @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	545783720
id	DOAJ041166302
language_de	englisch
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callnumber-first	R - Medicine
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spellingShingle	Shijie Li misc R858-859.7 misc RC321-571 misc Computer applications to medicine. Medical informatics misc Neurosciences. Biological psychiatry. Neuropsychiatry A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices
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title	A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices
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title_full	A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices
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author_browse	Shijie Li Xiaolong Shen Yong Dou Shice Ni Jinwei Xu Ke Yang Qiang Wang Xin Niu
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title_sort	novel memory-scheduling strategy for large convolutional neural network on memory-limited devices
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title_auth	A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices
abstract	Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices.
abstractGer	Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices.
abstract_unstemmed	Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices.
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title_short	A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices
url	https://doi.org/10.1155/2019/4328653 https://doaj.org/article/ac43dc8e2bbd4ce88a6ce879fb7d7611 http://dx.doi.org/10.1155/2019/4328653 https://doaj.org/toc/1687-5265 https://doaj.org/toc/1687-5273
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up_date	2024-07-03T18:46:25.604Z
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At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). 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Neuropsychiatry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaolong Shen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yong Dou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shice Ni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jinwei Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ke Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Qiang Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xin Niu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Computational Intelligence and Neuroscience</subfield><subfield code="d">Hindawi Limited, 2007</subfield><subfield code="g">(2019)</subfield><subfield code="w">(DE-627)545783720</subfield><subfield code="w">(DE-600)2388208-6</subfield><subfield code="x">16875265</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">year:2019</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1155/2019/4328653</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ac43dc8e2bbd4ce88a6ce879fb7d7611</subfield><subfield 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A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices

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