Paper Abstract and Keywords |
Presentation |
2021-03-26 10:40
Unsupervised Recycled FPGA Detection Using Direct Density Ratio Estimation Based on Self-referencing Yuya Isaka (KGU), Michihiro Shintani (NAIST), Foisal Ahmed (PU), Michiko Inoue (NAIST) CPSY2020-60 DC2020-90 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
It is well known that the performance of field-programmable gate-array (FPGA) degrades over time due to their usage. Several methods have been proposed in which the frequency of a ring oscillator (Ring oscillator, RO) designed on an FPGA is analyzed and whether it is recycled or not is determined by machine learning model. However, most of the existing methods require a large amount of fresh FPGAs as correct data for machine learning, which poses a problem in actual application to industrial. In this paper, we propose a novel unsupervised recycled FPGA detection method. In the proposed method, multi-stage ROs in all logical blocks are designed on the FPGA, and adjacent ROs are compared to reduce the effects of manufacturing process variation and highlight the effects of aging degradation in the measured frequencies, and then apply outlier detection through direct density ratio estimation. Through experiments using 10 fresh commercial FPGAs and 2 out of them that were aged, we demonstrate the proposed method can detect these two recycled FPGAs without misclassification. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Recycled FPGA detection / Manufacturing process variation / Unsupervised outlier detection / Direct density ratio estimation / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 436, DC2020-90, pp. 61-66, March 2021. |
Paper # |
DC2020-90 |
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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CPSY2020-60 DC2020-90 |
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 |
DC |
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) |
Unsupervised Recycled FPGA Detection Using Direct Density Ratio Estimation Based on Self-referencing |
Sub Title (in English) |
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Keyword(1) |
Recycled FPGA detection |
Keyword(2) |
Manufacturing process variation |
Keyword(3) |
Unsupervised outlier detection |
Keyword(4) |
Direct density ratio estimation |
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1st Author's Name |
Yuya Isaka |
1st Author's Affiliation |
Kwansei Gakuin University (KGU) |
2nd Author's Name |
Michihiro Shintani |
2nd Author's Affiliation |
Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST) |
3rd Author's Name |
Foisal Ahmed |
3rd Author's Affiliation |
Prime University (PU) |
4th Author's Name |
Michiko Inoue |
4th Author's Affiliation |
Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST) |
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Speaker |
Author-1 |
Date Time |
2021-03-26 10:40:00 |
Presentation Time |
20 minutes |
Registration for |
DC |
Paper # |
CPSY2020-60, DC2020-90 |
Volume (vol) |
vol.120 |
Number (no) |
no.435(CPSY), no.436(DC) |
Page |
pp.61-66 |
#Pages |
6 |
Date of Issue |
2021-03-18 (CPSY, DC) |
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