Characterizing convolutional neural network workloads on a detailed GPU simulator

Kwanghee Chang; Minsik Kim; Kyungah Kim; Won Woo Ro

doi:10.1109/ISOCC.2017.8368781

Characterizing convolutional neural network workloads on a detailed GPU simulator

Kwanghee Chang, Minsik Kim, Kyungah Kim, Won Woo Ro

Department of Electrical and Electronic Engineering

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

1 Citation (Scopus)

Abstract

Recent frameworks on convolutional neural networks (CNNs) such as Caffe and MXNet have focused primarily on being compatible with CUDA software and hardware application. However, it was designed for GPU architecture of compute capability 3.0 and above. Therefore, it needs verification of function to perform GPGPU-Sim which is implemented as NVIDIA compute capability devices 2.x. We developed a framework which can make inferencing AlexNet on GPGPU-Sim. We also analyze the execution results of the GPGPU-Sim. The number of lines in one set of the L1 data cache is sensitive to influence performance of AlexNet inference.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference 2017, ISOCC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	84-85
Number of pages	2
ISBN (Electronic)	9781538622858
DOIs	https://doi.org/10.1109/ISOCC.2017.8368781
Publication status	Published - 2018 May 29
Event	14th International SoC Design Conference, ISOCC 2017 - Seoul, Korea, Republic of Duration: 2017 Nov 5 → 2017 Nov 8

Publication series

Name	Proceedings - International SoC Design Conference 2017, ISOCC 2017

Other

Other	14th International SoC Design Conference, ISOCC 2017
Country/Territory	Korea, Republic of
City	Seoul
Period	17/11/5 → 17/11/8

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Access to Document

10.1109/ISOCC.2017.8368781

Cite this

Chang, K., Kim, M., Kim, K., & Ro, W. W. (2018). Characterizing convolutional neural network workloads on a detailed GPU simulator. In Proceedings - International SoC Design Conference 2017, ISOCC 2017 (pp. 84-85). (Proceedings - International SoC Design Conference 2017, ISOCC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC.2017.8368781

@inproceedings{2ec272bd3e2c4e19a184906de7dbba90,

title = "Characterizing convolutional neural network workloads on a detailed GPU simulator",

abstract = "Recent frameworks on convolutional neural networks (CNNs) such as Caffe and MXNet have focused primarily on being compatible with CUDA software and hardware application. However, it was designed for GPU architecture of compute capability 3.0 and above. Therefore, it needs verification of function to perform GPGPU-Sim which is implemented as NVIDIA compute capability devices 2.x. We developed a framework which can make inferencing AlexNet on GPGPU-Sim. We also analyze the execution results of the GPGPU-Sim. The number of lines in one set of the L1 data cache is sensitive to influence performance of AlexNet inference.",

keywords = "AlexNet, CUDA, Compute Capability, GPGPU-Sim, GPU",

author = "Kwanghee Chang and Minsik Kim and Kyungah Kim and Ro, {Won Woo}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th International SoC Design Conference, ISOCC 2017 ; Conference date: 05-11-2017 Through 08-11-2017",

year = "2018",

month = may,

day = "29",

doi = "10.1109/ISOCC.2017.8368781",

language = "English",

series = "Proceedings - International SoC Design Conference 2017, ISOCC 2017",

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Chang, K, Kim, M, Kim, K & Ro, WW 2018, Characterizing convolutional neural network workloads on a detailed GPU simulator. in Proceedings - International SoC Design Conference 2017, ISOCC 2017. Proceedings - International SoC Design Conference 2017, ISOCC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 84-85, 14th International SoC Design Conference, ISOCC 2017, Seoul, Korea, Republic of, 17/11/5. https://doi.org/10.1109/ISOCC.2017.8368781

Characterizing convolutional neural network workloads on a detailed GPU simulator. / Chang, Kwanghee; Kim, Minsik; Kim, Kyungah et al.
Proceedings - International SoC Design Conference 2017, ISOCC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 84-85 (Proceedings - International SoC Design Conference 2017, ISOCC 2017).

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

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AU - Chang, Kwanghee

AU - Kim, Minsik

AU - Kim, Kyungah

AU - Ro, Won Woo

PY - 2018/5/29

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N2 - Recent frameworks on convolutional neural networks (CNNs) such as Caffe and MXNet have focused primarily on being compatible with CUDA software and hardware application. However, it was designed for GPU architecture of compute capability 3.0 and above. Therefore, it needs verification of function to perform GPGPU-Sim which is implemented as NVIDIA compute capability devices 2.x. We developed a framework which can make inferencing AlexNet on GPGPU-Sim. We also analyze the execution results of the GPGPU-Sim. The number of lines in one set of the L1 data cache is sensitive to influence performance of AlexNet inference.

AB - Recent frameworks on convolutional neural networks (CNNs) such as Caffe and MXNet have focused primarily on being compatible with CUDA software and hardware application. However, it was designed for GPU architecture of compute capability 3.0 and above. Therefore, it needs verification of function to perform GPGPU-Sim which is implemented as NVIDIA compute capability devices 2.x. We developed a framework which can make inferencing AlexNet on GPGPU-Sim. We also analyze the execution results of the GPGPU-Sim. The number of lines in one set of the L1 data cache is sensitive to influence performance of AlexNet inference.

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ER -

Characterizing convolutional neural network workloads on a detailed GPU simulator

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