High Performance Implementation of 3D Convolutional Neural Networks on a GPU Article Swipe
Qiang Lan
,
Zelong Wang
,
Mei Wen
,
Chunyuan Zhang
,
Yijie Wang
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1155/2017/8348671
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1155/2017/8348671
Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2017/8348671
- http://downloads.hindawi.com/journals/cin/2017/8348671.pdf
- OA Status
- hybrid
- Cited By
- 30
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2767899175
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2767899175Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2017/8348671Digital Object Identifier
- Title
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High Performance Implementation of 3D Convolutional Neural Networks on a GPUWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-01-01Full publication date if available
- Authors
-
Qiang Lan, Zelong Wang, Mei Wen, Chunyuan Zhang, Yijie WangList of authors in order
- Landing page
-
https://doi.org/10.1155/2017/8348671Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cin/2017/8348671.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/cin/2017/8348671.pdfDirect OA link when available
- Concepts
-
Computer science, Convolutional neural network, Speedup, Computation, Convolution (computer science), Fast Fourier transform, Artificial intelligence, Artificial neural network, Auxiliary memory, Pattern recognition (psychology), Parallel computing, Algorithm, Computer hardwareTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2, 2023: 2, 2022: 1, 2021: 9Per-year citation counts (last 5 years)
- References (count)
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7Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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