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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
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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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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)  
Keyword(1) SpMM  
Keyword(2) Sparse Neural Network  
Keyword(3) 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
Date of Issue 2021-03-18 (CPSY, DC) 


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