Accelerating forwarding computation of artificial neural network using CUDA

Jong Hyun Park; Won Woo Ro

doi:10.1109/ELINFOCOM.2016.7562974

Accelerating forwarding computation of artificial neural network using CUDA

Jong Hyun Park, Won Woo Ro

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Recently, graphics processing units (GPUs) are widely used for accelerating general purpose workloads using programming models such as open computing language (OpenCL) or compute unified device architecture (CUDA). In this paper, we accelerated the Artificial Neural Network (ANN) algorithm, one of the popular algorithm in machine learning and cognitive science, since the ANN algorithm needs to be faster for solving more complex problem or operating in real-time. The ANN algorithm has great potential for GPU acceleration since it is constructed with large data-parallel computations. We implemented forwarding computation of ANN in CUDA and optimized it using scratchpad memory of GPUs and leveraging the thread block size. As a results, our method shows 2.32 times faster performance compared to conventional CPU.

Original language	English
Title of host publication	International Conference on Electronics, Information, and Communications, ICEIC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467380164
DOIs	https://doi.org/10.1109/ELINFOCOM.2016.7562974
Publication status	Published - 2016 Sept 7
Event	15th International Conference on Electronics, Information, and Communications, ICEIC 2016 - Danang, Viet Nam Duration: 2016 Jan 27 → 2016 Jan 30

Publication series

Name	International Conference on Electronics, Information, and Communications, ICEIC 2016

Other

Other	15th International Conference on Electronics, Information, and Communications, ICEIC 2016
Country/Territory	Viet Nam
City	Danang
Period	16/1/27 → 16/1/30

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Control and Systems Engineering

Access to Document

10.1109/ELINFOCOM.2016.7562974

Cite this

Park, J. H., & Ro, W. W. (2016). Accelerating forwarding computation of artificial neural network using CUDA. In International Conference on Electronics, Information, and Communications, ICEIC 2016 Article 7562974 (International Conference on Electronics, Information, and Communications, ICEIC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ELINFOCOM.2016.7562974

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title = "Accelerating forwarding computation of artificial neural network using CUDA",

abstract = "Recently, graphics processing units (GPUs) are widely used for accelerating general purpose workloads using programming models such as open computing language (OpenCL) or compute unified device architecture (CUDA). In this paper, we accelerated the Artificial Neural Network (ANN) algorithm, one of the popular algorithm in machine learning and cognitive science, since the ANN algorithm needs to be faster for solving more complex problem or operating in real-time. The ANN algorithm has great potential for GPU acceleration since it is constructed with large data-parallel computations. We implemented forwarding computation of ANN in CUDA and optimized it using scratchpad memory of GPUs and leveraging the thread block size. As a results, our method shows 2.32 times faster performance compared to conventional CPU.",

keywords = "ANN, CUDA, GPGPU",

author = "Park, {Jong Hyun} and Ro, {Won Woo}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 15th International Conference on Electronics, Information, and Communications, ICEIC 2016 ; Conference date: 27-01-2016 Through 30-01-2016",

year = "2016",

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doi = "10.1109/ELINFOCOM.2016.7562974",

language = "English",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Park, JH & Ro, WW 2016, Accelerating forwarding computation of artificial neural network using CUDA. in International Conference on Electronics, Information, and Communications, ICEIC 2016., 7562974, International Conference on Electronics, Information, and Communications, ICEIC 2016, Institute of Electrical and Electronics Engineers Inc., 15th International Conference on Electronics, Information, and Communications, ICEIC 2016, Danang, Viet Nam, 16/1/27. https://doi.org/10.1109/ELINFOCOM.2016.7562974

Accelerating forwarding computation of artificial neural network using CUDA. / Park, Jong Hyun; Ro, Won Woo.
International Conference on Electronics, Information, and Communications, ICEIC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7562974 (International Conference on Electronics, Information, and Communications, ICEIC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Accelerating forwarding computation of artificial neural network using CUDA

AU - Park, Jong Hyun

AU - Ro, Won Woo

PY - 2016/9/7

Y1 - 2016/9/7

N2 - Recently, graphics processing units (GPUs) are widely used for accelerating general purpose workloads using programming models such as open computing language (OpenCL) or compute unified device architecture (CUDA). In this paper, we accelerated the Artificial Neural Network (ANN) algorithm, one of the popular algorithm in machine learning and cognitive science, since the ANN algorithm needs to be faster for solving more complex problem or operating in real-time. The ANN algorithm has great potential for GPU acceleration since it is constructed with large data-parallel computations. We implemented forwarding computation of ANN in CUDA and optimized it using scratchpad memory of GPUs and leveraging the thread block size. As a results, our method shows 2.32 times faster performance compared to conventional CPU.

AB - Recently, graphics processing units (GPUs) are widely used for accelerating general purpose workloads using programming models such as open computing language (OpenCL) or compute unified device architecture (CUDA). In this paper, we accelerated the Artificial Neural Network (ANN) algorithm, one of the popular algorithm in machine learning and cognitive science, since the ANN algorithm needs to be faster for solving more complex problem or operating in real-time. The ANN algorithm has great potential for GPU acceleration since it is constructed with large data-parallel computations. We implemented forwarding computation of ANN in CUDA and optimized it using scratchpad memory of GPUs and leveraging the thread block size. As a results, our method shows 2.32 times faster performance compared to conventional CPU.

KW - ANN

KW - CUDA

KW - GPGPU

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M3 - Conference contribution

AN - SCOPUS:84988799967

T3 - International Conference on Electronics, Information, and Communications, ICEIC 2016

BT - International Conference on Electronics, Information, and Communications, ICEIC 2016

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Park JH, Ro WW. Accelerating forwarding computation of artificial neural network using CUDA. In International Conference on Electronics, Information, and Communications, ICEIC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7562974. (International Conference on Electronics, Information, and Communications, ICEIC 2016). doi: 10.1109/ELINFOCOM.2016.7562974

Accelerating forwarding computation of artificial neural network using CUDA

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