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EnglishConvolution is most computationally intensive task of Convolutional Neural Network(CNN). It demands both computational power and memory storage of processing unit. There are different approaches to compute the solution of convolution. In this paper, matrix multiplication based convolution(ConvMM) approach is implemented and accelerated using concurrent resources of Graphics Processing Unit(GPU). CUDA computing language is used to implement this layer. Performance of this GPU-only convolutional layer is compared with its heterogeneous version. Further, flow of this GPU-only convolutional layer is optimized using Unified memory by eliminating overhead caused by extra memory transfers.
| Titolo: | GPU-only unified ConvMM layer for neural classifiers |
| Autori: | |
| Data di pubblicazione: | 2017 |
| Abstract: | Convolution is most computationally intensive task of Convolutional Neural Network(CNN). It deman...ds both computational power and memory storage of processing unit. There are different approaches to compute the solution of convolution. In this paper, matrix multiplication based convolution(ConvMM) approach is implemented and accelerated using concurrent resources of Graphics Processing Unit(GPU). CUDA computing language is used to implement this layer. Performance of this GPU-only convolutional layer is compared with its heterogeneous version. Further, flow of this GPU-only convolutional layer is optimized using Unified memory by eliminating overhead caused by extra memory transfers. |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2675423
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