Recursive least squares method for training and pruning convolutional neural networks
Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative struc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Tianzong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2023), 20 vom: 26. Juli, Seite 24603-24618 |
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| Übergeordnetes Werk: |
volume:53 ; year:2023 ; number:20 ; day:26 ; month:07 ; pages:24603-24618 |
| Links: |
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| DOI / URN: |
10.1007/s10489-023-04740-z |
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| Katalog-ID: |
SPR053488164 |
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| 100 | 1 | |a Yu, Tianzong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Recursive least squares method for training and pruning convolutional neural networks |
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| 520 | |a Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. | ||
| 650 | 4 | |a Convolutional neural network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Model compression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Structured pruning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Iterative pruning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Recursive least squares |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhang, Chunyuan |0 (orcid)0000-0002-2785-0510 |4 aut | |
| 700 | 1 | |a Ma, Meng |4 aut | |
| 700 | 1 | |a Wang, Yuan |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied intelligence |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 |g 53(2023), 20 vom: 26. Juli, Seite 24603-24618 |w (DE-627)271180919 |w (DE-600)1479519-X |x 1573-7497 |7 nnns |
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| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10489-023-04740-z |z kostenfrei |3 Volltext |
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10.1007/s10489-023-04740-z doi (DE-627)SPR053488164 (SPR)s10489-023-04740-z-e DE-627 ger DE-627 rakwb eng Yu, Tianzong verfasserin aut Recursive least squares method for training and pruning convolutional neural networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. Convolutional neural network (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Structured pruning (dpeaa)DE-He213 Iterative pruning (dpeaa)DE-He213 Recursive least squares (dpeaa)DE-He213 Zhang, Chunyuan (orcid)0000-0002-2785-0510 aut Ma, Meng aut Wang, Yuan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 20 vom: 26. Juli, Seite 24603-24618 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:20 day:26 month:07 pages:24603-24618 https://dx.doi.org/10.1007/s10489-023-04740-z kostenfrei 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_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_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_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_4393 GBV_ILN_4700 AR 53 2023 20 26 07 24603-24618 |
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10.1007/s10489-023-04740-z doi (DE-627)SPR053488164 (SPR)s10489-023-04740-z-e DE-627 ger DE-627 rakwb eng Yu, Tianzong verfasserin aut Recursive least squares method for training and pruning convolutional neural networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. Convolutional neural network (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Structured pruning (dpeaa)DE-He213 Iterative pruning (dpeaa)DE-He213 Recursive least squares (dpeaa)DE-He213 Zhang, Chunyuan (orcid)0000-0002-2785-0510 aut Ma, Meng aut Wang, Yuan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 20 vom: 26. Juli, Seite 24603-24618 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:20 day:26 month:07 pages:24603-24618 https://dx.doi.org/10.1007/s10489-023-04740-z kostenfrei 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_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_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_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_4393 GBV_ILN_4700 AR 53 2023 20 26 07 24603-24618 |
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10.1007/s10489-023-04740-z doi (DE-627)SPR053488164 (SPR)s10489-023-04740-z-e DE-627 ger DE-627 rakwb eng Yu, Tianzong verfasserin aut Recursive least squares method for training and pruning convolutional neural networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. Convolutional neural network (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Structured pruning (dpeaa)DE-He213 Iterative pruning (dpeaa)DE-He213 Recursive least squares (dpeaa)DE-He213 Zhang, Chunyuan (orcid)0000-0002-2785-0510 aut Ma, Meng aut Wang, Yuan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 20 vom: 26. Juli, Seite 24603-24618 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:20 day:26 month:07 pages:24603-24618 https://dx.doi.org/10.1007/s10489-023-04740-z kostenfrei 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_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_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_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_4393 GBV_ILN_4700 AR 53 2023 20 26 07 24603-24618 |
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10.1007/s10489-023-04740-z doi (DE-627)SPR053488164 (SPR)s10489-023-04740-z-e DE-627 ger DE-627 rakwb eng Yu, Tianzong verfasserin aut Recursive least squares method for training and pruning convolutional neural networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. Convolutional neural network (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Structured pruning (dpeaa)DE-He213 Iterative pruning (dpeaa)DE-He213 Recursive least squares (dpeaa)DE-He213 Zhang, Chunyuan (orcid)0000-0002-2785-0510 aut Ma, Meng aut Wang, Yuan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 20 vom: 26. Juli, Seite 24603-24618 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:20 day:26 month:07 pages:24603-24618 https://dx.doi.org/10.1007/s10489-023-04740-z kostenfrei 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_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_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_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_4393 GBV_ILN_4700 AR 53 2023 20 26 07 24603-24618 |
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10.1007/s10489-023-04740-z doi (DE-627)SPR053488164 (SPR)s10489-023-04740-z-e DE-627 ger DE-627 rakwb eng Yu, Tianzong verfasserin aut Recursive least squares method for training and pruning convolutional neural networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. Convolutional neural network (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Structured pruning (dpeaa)DE-He213 Iterative pruning (dpeaa)DE-He213 Recursive least squares (dpeaa)DE-He213 Zhang, Chunyuan (orcid)0000-0002-2785-0510 aut Ma, Meng aut Wang, Yuan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 20 vom: 26. Juli, Seite 24603-24618 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:20 day:26 month:07 pages:24603-24618 https://dx.doi.org/10.1007/s10489-023-04740-z kostenfrei 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_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_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_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_4393 GBV_ILN_4700 AR 53 2023 20 26 07 24603-24618 |
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However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. 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Yu, Tianzong |
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recursive least squares method for training and pruning convolutional neural networks |
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Recursive least squares method for training and pruning convolutional neural networks |
| abstract |
Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. © The Author(s) 2023 |
| abstractGer |
Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. © The Author(s) 2023 |
| abstract_unstemmed |
Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer. © The Author(s) 2023 |
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| title_short |
Recursive least squares method for training and pruning convolutional neural networks |
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https://dx.doi.org/10.1007/s10489-023-04740-z |
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| author2 |
Zhang, Chunyuan Ma, Meng Wang, Yuan |
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Zhang, Chunyuan Ma, Meng Wang, Yuan |
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| doi_str |
10.1007/s10489-023-04740-z |
| up_date |
2024-07-03T19:52:54.756Z |
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1803588861700341760 |
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| score |
7.4007416 |