Paper Abstract and Keywords |
Presentation |
2021-09-10 10:20
Convolutional neural network implementations using Vitis AI Akihiko Ushiroyama, Nobuya Watanabe, Akira Nagoya, Minoru Watanabe (Okayama Univ.) RECONF2021-19 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, Xilinx provides an FPGA-based Vitis AI development environment which is one of deep learning frameworks to accelerate AI operations and to search a suitable neural network construction for a target application.
In this paper, we've implemented three types of convolutional neural networks onto the Vitis AI development environment and evaluated the performance, power consumption, lines of code, and so on.
As a result, we have confirmed the advantages of the Vitis AI. For example, the energy consumption of the FPGA platform is 5.06 times lower than that of a GPU. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Convolutional Neural Network (CNN) / FPGA / Vitis AI / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 175, RECONF2021-19, pp. 13-18, Sept. 2021. |
Paper # |
RECONF2021-19 |
Date of Issue |
2021-09-03 (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) |
Notes on Review |
This article is a technical report without peer review, and its polished version will be published elsewhere. |
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RECONF2021-19 |
Conference Information |
Committee |
RECONF |
Conference Date |
2021-09-10 - 2021-09-10 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Reconfigurable system, etc. |
Paper Information |
Registration To |
RECONF |
Conference Code |
2021-09-RECONF |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Convolutional neural network implementations using Vitis AI |
Sub Title (in English) |
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Keyword(1) |
Convolutional Neural Network (CNN) |
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FPGA |
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Vitis AI |
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1st Author's Name |
Akihiko Ushiroyama |
1st Author's Affiliation |
Okayama University (Okayama Univ.) |
2nd Author's Name |
Nobuya Watanabe |
2nd Author's Affiliation |
Okayama University (Okayama Univ.) |
3rd Author's Name |
Akira Nagoya |
3rd Author's Affiliation |
Okayama University (Okayama Univ.) |
4th Author's Name |
Minoru Watanabe |
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Okayama University (Okayama Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-09-10 10:20:00 |
Presentation Time |
25 minutes |
Registration for |
RECONF |
Paper # |
RECONF2021-19 |
Volume (vol) |
vol.121 |
Number (no) |
no.175 |
Page |
pp.13-18 |
#Pages |
6 |
Date of Issue |
2021-09-03 (RECONF) |
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