The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Fast Algorithms for Convolutional Neural Networks
Abstract
Deep convolutional neural networks take GPU-days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3 3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64.
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Citations
Cites
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Cited by
Rethinking the Inception Architecture for Computer Vision
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TensorFlow: A system for large-scale machine learning
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Recent advances in convolutional neural networks
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Efficient Processing of Deep Neural Networks: A Tutorial and Survey
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“Other approaches include Strassen [75] and Winograd [76], which rearrange the computation such that the number of multiplications reduce from O(N(3)) to O(N) and by 2.”
Metadata
- Authors
- Lavin, A., & Gray, S.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4013-4021
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2172654076
- Cited by
- 915
- References
- 16
- FWCI
- 29.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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