Pruning and quantization algorithm with applications in memristor-based convolutional neural network
Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex application...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Guo, Mei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Cognitive neurodynamics - Dordrecht [u.a.] : Springer, 2007, 18(2023), 1 vom: 19. Jan., Seite 233-245 |
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| Übergeordnetes Werk: |
volume:18 ; year:2023 ; number:1 ; day:19 ; month:01 ; pages:233-245 |
| Links: |
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| DOI / URN: |
10.1007/s11571-022-09927-7 |
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| Katalog-ID: |
SPR054854814 |
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| 520 | |a Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. | ||
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| 650 | 4 | |a Network pruning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Quantization weight |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sun, Yurui |4 aut | |
| 700 | 1 | |a Zhu, Yongliang |4 aut | |
| 700 | 1 | |a Han, Mingqiao |4 aut | |
| 700 | 1 | |a Dou, Gang |4 aut | |
| 700 | 1 | |a Wen, Shiping |4 aut | |
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10.1007/s11571-022-09927-7 doi (DE-627)SPR054854814 (SPR)s11571-022-09927-7-e DE-627 ger DE-627 rakwb eng Guo, Mei verfasserin aut Pruning and quantization algorithm with applications in memristor-based convolutional neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. Memristor (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Network pruning (dpeaa)DE-He213 Quantization weight (dpeaa)DE-He213 Sun, Yurui aut Zhu, Yongliang aut Han, Mingqiao aut Dou, Gang aut Wen, Shiping aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 18(2023), 1 vom: 19. Jan., Seite 233-245 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:18 year:2023 number:1 day:19 month:01 pages:233-245 https://dx.doi.org/10.1007/s11571-022-09927-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 19 01 233-245 |
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10.1007/s11571-022-09927-7 doi (DE-627)SPR054854814 (SPR)s11571-022-09927-7-e DE-627 ger DE-627 rakwb eng Guo, Mei verfasserin aut Pruning and quantization algorithm with applications in memristor-based convolutional neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. Memristor (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Network pruning (dpeaa)DE-He213 Quantization weight (dpeaa)DE-He213 Sun, Yurui aut Zhu, Yongliang aut Han, Mingqiao aut Dou, Gang aut Wen, Shiping aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 18(2023), 1 vom: 19. Jan., Seite 233-245 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:18 year:2023 number:1 day:19 month:01 pages:233-245 https://dx.doi.org/10.1007/s11571-022-09927-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 19 01 233-245 |
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10.1007/s11571-022-09927-7 doi (DE-627)SPR054854814 (SPR)s11571-022-09927-7-e DE-627 ger DE-627 rakwb eng Guo, Mei verfasserin aut Pruning and quantization algorithm with applications in memristor-based convolutional neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. Memristor (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Network pruning (dpeaa)DE-He213 Quantization weight (dpeaa)DE-He213 Sun, Yurui aut Zhu, Yongliang aut Han, Mingqiao aut Dou, Gang aut Wen, Shiping aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 18(2023), 1 vom: 19. Jan., Seite 233-245 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:18 year:2023 number:1 day:19 month:01 pages:233-245 https://dx.doi.org/10.1007/s11571-022-09927-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 19 01 233-245 |
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10.1007/s11571-022-09927-7 doi (DE-627)SPR054854814 (SPR)s11571-022-09927-7-e DE-627 ger DE-627 rakwb eng Guo, Mei verfasserin aut Pruning and quantization algorithm with applications in memristor-based convolutional neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. Memristor (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Network pruning (dpeaa)DE-He213 Quantization weight (dpeaa)DE-He213 Sun, Yurui aut Zhu, Yongliang aut Han, Mingqiao aut Dou, Gang aut Wen, Shiping aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 18(2023), 1 vom: 19. Jan., Seite 233-245 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:18 year:2023 number:1 day:19 month:01 pages:233-245 https://dx.doi.org/10.1007/s11571-022-09927-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 19 01 233-245 |
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10.1007/s11571-022-09927-7 doi (DE-627)SPR054854814 (SPR)s11571-022-09927-7-e DE-627 ger DE-627 rakwb eng Guo, Mei verfasserin aut Pruning and quantization algorithm with applications in memristor-based convolutional neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. Memristor (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Network pruning (dpeaa)DE-He213 Quantization weight (dpeaa)DE-He213 Sun, Yurui aut Zhu, Yongliang aut Han, Mingqiao aut Dou, Gang aut Wen, Shiping aut Enthalten in Cognitive neurodynamics Dordrecht [u.a.] : Springer, 2007 18(2023), 1 vom: 19. Jan., Seite 233-245 (DE-627)527576689 (DE-600)2276890-7 1871-4099 nnns volume:18 year:2023 number:1 day:19 month:01 pages:233-245 https://dx.doi.org/10.1007/s11571-022-09927-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 19 01 233-245 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Memristor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convolutional neural network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Network pruning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quantization weight</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Yurui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Yongliang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Mingqiao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dou, Gang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wen, Shiping</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Cognitive neurodynamics</subfield><subfield code="d">Dordrecht [u.a.] : Springer, 2007</subfield><subfield code="g">18(2023), 1 vom: 19. Jan., Seite 233-245</subfield><subfield code="w">(DE-627)527576689</subfield><subfield code="w">(DE-600)2276890-7</subfield><subfield code="x">1871-4099</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:18</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:19</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:233-245</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11571-022-09927-7</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" 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Guo, Mei |
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Guo, Mei misc Memristor misc Convolutional neural network misc Network pruning misc Quantization weight Pruning and quantization algorithm with applications in memristor-based convolutional neural network |
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Pruning and quantization algorithm with applications in memristor-based convolutional neural network Memristor (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Network pruning (dpeaa)DE-He213 Quantization weight (dpeaa)DE-He213 |
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pruning and quantization algorithm with applications in memristor-based convolutional neural network |
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Pruning and quantization algorithm with applications in memristor-based convolutional neural network |
| abstract |
Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Pruning and quantization algorithm with applications in memristor-based convolutional neural network |
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https://dx.doi.org/10.1007/s11571-022-09927-7 |
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| author2 |
Sun, Yurui Zhu, Yongliang Han, Mingqiao Dou, Gang Wen, Shiping |
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Sun, Yurui Zhu, Yongliang Han, Mingqiao Dou, Gang Wen, Shiping |
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| doi_str |
10.1007/s11571-022-09927-7 |
| up_date |
2024-07-04T03:16:32.416Z |
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| score |
7.402521 |