Paper Abstract and Keywords |
Presentation |
2021-03-25 14:40
Parallelization and Vectorization of SpMM for Sparse Neural Network Yuta Tadokoro, Keiji Kimura, Hironori Kasahara (Waseda Univ.) CPSY2020-55 DC2020-85 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Pruning is one of the well-known model compression techniques in Deep Learning. Eliminating less important weights in the model provides a smaller model size than the original one while keeping high accuracy. As a result of the pruning, the weight matrices are represented as sparse matrices. However, the sparse matrices obtained by pruning
are highly randomized, unlike the sparse matrices used in scientific applications. Thus it is difficult to employ acceleration techniques for them relying on the locality of non-zero elements. This paper proposes a method to accelerate SpMM (Sparse Matrix - Dense Matrix Multiplication) for sparse matrices with high randomness. The proposed method is applied to ResNet50 and evaluated on NEC SX-Aurora TSUBASA. The speed-ups were 2.78 times with one processor core for the layer to which the proposed method was used and 1.98 times with eight processor cores for the whole model. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
SpMM / Sparse Neural Network / Vector Processor / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 435, CPSY2020-55, pp. 31-36, March 2021. |
Paper # |
CPSY2020-55 |
Date of Issue |
2021-03-18 (CPSY, DC) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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CPSY2020-55 DC2020-85 |
Conference Information |
Committee |
CPSY DC IPSJ-SLDM IPSJ-EMB IPSJ-ARC |
Conference Date |
2021-03-25 - 2021-03-26 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
ETNET2021 |
Paper Information |
Registration To |
CPSY |
Conference Code |
2021-03-CPSY-DC-SLDM-EMB-ARC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Parallelization and Vectorization of SpMM for Sparse Neural Network |
Sub Title (in English) |
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SpMM |
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Sparse Neural Network |
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Vector Processor |
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1st Author's Name |
Yuta Tadokoro |
1st Author's Affiliation |
Waseda University (Waseda Univ.) |
2nd Author's Name |
Keiji Kimura |
2nd Author's Affiliation |
Waseda University (Waseda Univ.) |
3rd Author's Name |
Hironori Kasahara |
3rd Author's Affiliation |
Waseda University (Waseda Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-03-25 14:40:00 |
Presentation Time |
20 minutes |
Registration for |
CPSY |
Paper # |
CPSY2020-55, DC2020-85 |
Volume (vol) |
vol.120 |
Number (no) |
no.435(CPSY), no.436(DC) |
Page |
pp.31-36 |
#Pages |
6 |
Date of Issue |
2021-03-18 (CPSY, DC) |
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