Paper Abstract and Keywords |
Presentation |
2022-01-24 15:55
Accelerating Deep Neural Networks on Edge Devices by Knowledge Distillation and Layer Pruning Yuki Ichikawa, Akira Jinguji, Ryosuke Kuramochi, Hiroki Nakahara (Titech) VLD2021-58 CPSY2021-27 RECONF2021-66 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
A deep neural network (DNN) is computationally expensive, making it challenging to run DNN on edge devices. Therefore, model compression techniques such as knowledge distillation and pruning are proposed. This research suggests an efficient method to compress pretrained models using these techniques. We show that our method can compress models for edge devices in a short time. We also show a trade--off between recognition accuracy and inference time on Jetson Nano GPU and DPU on a Xilinx FPGA. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Knowledge Distillation / Layer Pruning / Deep Neural Network / Edge Device / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 344, RECONF2021-66, pp. 49-54, Jan. 2022. |
Paper # |
RECONF2021-66 |
Date of Issue |
2022-01-17 (VLD, CPSY, RECONF) |
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) |
Download PDF |
VLD2021-58 CPSY2021-27 RECONF2021-66 |
Conference Information |
Committee |
RECONF VLD CPSY IPSJ-ARC IPSJ-SLDM |
Conference Date |
2022-01-24 - 2022-01-25 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
FPGA Applications, etc. |
Paper Information |
Registration To |
RECONF |
Conference Code |
2022-01-RECONF-VLD-CPSY-ARC-SLDM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Accelerating Deep Neural Networks on Edge Devices by Knowledge Distillation and Layer Pruning |
Sub Title (in English) |
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Knowledge Distillation |
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Layer Pruning |
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Deep Neural Network |
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Edge Device |
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1st Author's Name |
Yuki Ichikawa |
1st Author's Affiliation |
Tokyo Institute of Technology (Titech) |
2nd Author's Name |
Akira Jinguji |
2nd Author's Affiliation |
Tokyo Institute of Technology (Titech) |
3rd Author's Name |
Ryosuke Kuramochi |
3rd Author's Affiliation |
Tokyo Institute of Technology (Titech) |
4th Author's Name |
Hiroki Nakahara |
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Tokyo Institute of Technology (Titech) |
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Speaker |
Author-1 |
Date Time |
2022-01-24 15:55:00 |
Presentation Time |
25 minutes |
Registration for |
RECONF |
Paper # |
VLD2021-58, CPSY2021-27, RECONF2021-66 |
Volume (vol) |
vol.121 |
Number (no) |
no.342(VLD), no.343(CPSY), no.344(RECONF) |
Page |
pp.49-54 |
#Pages |
6 |
Date of Issue |
2022-01-17 (VLD, CPSY, RECONF) |
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