Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2014
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909135148154880 |
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| author | Denton, Remi Zaremba, Wojciech Bruna, Joan LeCun, Yann Fergus, Rob |
| author_facet | Denton, Remi Zaremba, Wojciech Bruna, Joan LeCun, Yann Fergus, Rob |
| contents | We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters problematic. The computation is dominated by the convolution operations in the lower layers of the model. We exploit the linear structure present within the convolutional filters to derive approximations that significantly reduce the required computation. Using large state-of-the-art models, we demonstrate we demonstrate speedups of convolutional layers on both CPU and GPU by a factor of 2x, while keeping the accuracy within 1% of the original model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1404_0736 |
| institution | arXiv |
| publishDate | 2014 |
| record_format | arxiv |
| spellingShingle | Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Denton, Remi Zaremba, Wojciech Bruna, Joan LeCun, Yann Fergus, Rob Computer Vision and Pattern Recognition Machine Learning We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters problematic. The computation is dominated by the convolution operations in the lower layers of the model. We exploit the linear structure present within the convolutional filters to derive approximations that significantly reduce the required computation. Using large state-of-the-art models, we demonstrate we demonstrate speedups of convolutional layers on both CPU and GPU by a factor of 2x, while keeping the accuracy within 1% of the original model. |
| title | Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/1404.0736 |