Efficient convolution pooling on the GPU

Suita Shunsuke

Nishimura Takahiro

Tokura Hiroki

Nakano Koji

Itou Yasuaki

Kasagi Akihiko

Tabaru Tsuguchika

Journal of Parallel and Distributed Computing

The main contribution of this paper is to show efficient implementations of the convolution-pooling in the GPU, in which the pooling follows the multiple convolution. Since the multiple convolution and the pooling operations are performed alternately in earlier stages of many Convolutional Neural Networks (CNNs), it is very important to accelerate the convolution-pooling. Our new GPU implementation uses two techniques, (1) convolution interchange with direct sum, and (2) conversion to matrix multiplication. By these techniques, the computational and memory access cost are reduced. Further the convolution interchange is converted to matrix multiplication, which can be computed by cuBLAS very efficiently. Experimental results using Tesla V100 GPU show that our new GPU implementation compatible with cuDNN for the convolution-pooling is expected 2.90 times and 1.43 times faster for fp32 and fp16 than the multiple convolution and then the pooling by cuDNN, respectively. the most popular library of primitives to implement the CNNs in the GPU.

Deep learning

Neural Networks

Convolution

Average pooling

GPU

英語

Elsevier

© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

This is not the published version. Please cite only the published version. この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。

[ISSN] 0743-7315

[DOI] 10.1016/j.jpdc.2019.12.006

[DOI] https://doi.org/10.1016/j.jpdc.2019.12.006