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| Presentation | 2021-01-25 15:15 A High-speed Convolutional Neural Network Accelerator for an Adaptive Resolution on an FPGA Koki Sayama, Akira Jinguji, Naoto Soga, Hiroki Nakahara (Tokyo Tech)  VLD2020-49 CPSY2020-32 RECONF2020-68 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, CNN has been used for various tasks in the field of computer vision and has achievedexcellent performance. However, the computational complexity of these convolutional operations is enormous. Weinvestigate the resolution reduction of the input image as a method to reduce the costs (computation complexity andrequired buffer size) of CNN and discuss the trade-off between classification accuracy and resolution. We propose ahighly parallelized CNN accelerator in the horizontal, vertical and channel directions. The parallelism parameterizedin each direction is scalable to the input resolution. It provides hardware that maximizes computational and resourceefficiency depending on a given input image resolution. We found the accuracy decrease is small even if the inputresolution is lower than the standard resolution of2242in the model based on MobileNetV2. As an example, at1282resolutions, the model achieves 64.2% (Top-1) accuracy on ImageNet and computational costs are reduced to about1/3 for a 7.3% decrease compared to the standard resolution case. Also, we propose a highly parallelized high-speedCNN accelerator with resolution scalable. The accelerator with spatial-parallelism parameterized is scalable to theinput resolution. The scalability enables efficient computation on various circuit scales for each resolution. We haveimplemented a low-resolution CNN based on MobileNetV2 on an FPGA board. The inference speed achieves framesper second by 17.0 times compared with CPU. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Convolutional Neural Networks / hardware accelerator / FPGA / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 339, RECONF2020-68, pp. 58-62, Jan. 2021. | |
| Paper # | RECONF2020-68 | |
| Date of Issue | 2021-01-18 (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 | VLD2020-49 CPSY2020-32 RECONF2020-68 |
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| Conference Information | |
| Committee | CPSY RECONF VLD IPSJ-ARC IPSJ-SLDM |
| Conference Date | 2021-01-25 - 2021-01-26 |
| 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 | 2021-01-CPSY-RECONF-VLD-ARC-SLDM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A High-speed Convolutional Neural Network Accelerator for an Adaptive Resolution on an FPGA |
| Sub Title (in English) | |
| Keyword(1) | Convolutional Neural Networks |
| Keyword(2) | hardware accelerator |
| Keyword(3) | FPGA |
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| 1st Author's Name | Koki Sayama |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 2nd Author's Name | Akira Jinguji |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 3rd Author's Name | Naoto Soga |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 4th Author's Name | Hiroki Nakahara |
| 4th Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
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| Speaker | Author-1 |
| Date Time | 2021-01-25 15:15:00 |
| Presentation Time | 25 minutes |
| Registration for | RECONF |
| Paper # | VLD2020-49, CPSY2020-32, RECONF2020-68 |
| Volume (vol) | vol.120 |
| Number (no) | no.337(VLD), no.338(CPSY), no.339(RECONF) |
| Page | pp.58-62 |
| #Pages | 5 |
| Date of Issue | 2021-01-18 (VLD, CPSY, RECONF) |
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