research-article

HTcatcher: Finite State Machine and Feature Verifcation for Large-scale Neuromorphic Computing Systems

Authors:

Tsung-Yi HoAuthors Info & Claims

GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI

Pages 415 - 420

https://doi.org/10.1145/3386263.3406955

Published: 07 September 2020 Publication History

Abstract

Recent advances in resistive synaptic devices have enabled the emergence of brain-inspired smart chips. These chips can execute complex cognitive tasks in digital signal processing precisely and efficiently using an efficient neuromorphic system. The neuromorphic synapses used in such chips, however, are very sensitive to the external environment, thereby weakening their resistance to malicious modifications such as hardware Trojans and backdoors. Accordingly, in this paper, we propose HTcatcher, a security verification technique for hardware threat detection in neuromorphic computing systems, incorporating finite state machine and feature verification simultaneously, which has never been considered in prior work. Furthermore, we propose a pseudo-random matrix verifying technique for memory optimization, which can reduce the memory overhead of the multi-dimensional features in the system significantly. Experimental results confirm that the proposed method can identify the malicious modifications in the system accurately, while reducing the memory usage by 25%-50%.

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Cited By

Naveenkumar RSivamangai N(2024)Hardware Trojans Detection and Prevention Techniques ReviewWireless Personal Communications10.1007/s11277-024-11334-6136:2(1147-1182)Online publication date: 25-Jun-2024
https://doi.org/10.1007/s11277-024-11334-6
Dong CYao YXu YLiu XWang YZhang HXu L(2023)A Cost-Driven Method for Deep-Learning-Based Hardware Trojan DetectionSensors10.3390/s2312550323:12(5503)Online publication date: 11-Jun-2023
https://doi.org/10.3390/s23125503
Ma PWang ZWang Y(2023)A Pre-Silicon Detection Based on Deep Learning Model for Hardware TrojansJournal of Circuits, Systems and Computers10.1142/S021812662450144533:08Online publication date: 8-Dec-2023
https://doi.org/10.1142/S0218126624501445
Show More Cited By

Index Terms

HTcatcher: Finite State Machine and Feature Verifcation for Large-scale Neuromorphic Computing Systems
1. Hardware
  1. Emerging technologies
    1. Biology-related information processing
      1. Neural systems
2. Security and privacy
  1. Security in hardware
    1. Embedded systems security
    2. Hardware attacks and countermeasures
      1. Malicious design modifications

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cover image ACM Other conferences

GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI

September 2020

597 pages

ISBN:9781450379441

DOI:10.1145/3386263

General Chairs:
Tinoosh Mohsenin
University of Maryland, Baltimore County, USA
,
Weisheng Zhao
Beihang University, China
,
Program Chairs:
Yiran Chen
Duke University, USA
,
Onur Mutlu
ETH Zurich, Switzerland

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 September 2020

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GLSVLSI '20

GLSVLSI '20: Great Lakes Symposium on VLSI 2020

September 7 - 9, 2020

Virtual Event, China

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Overall Acceptance Rate 312 of 1,156 submissions, 27%

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Cited By

Naveenkumar RSivamangai N(2024)Hardware Trojans Detection and Prevention Techniques ReviewWireless Personal Communications10.1007/s11277-024-11334-6136:2(1147-1182)Online publication date: 25-Jun-2024
https://doi.org/10.1007/s11277-024-11334-6
Dong CYao YXu YLiu XWang YZhang HXu L(2023)A Cost-Driven Method for Deep-Learning-Based Hardware Trojan DetectionSensors10.3390/s2312550323:12(5503)Online publication date: 11-Jun-2023
https://doi.org/10.3390/s23125503
Ma PWang ZWang Y(2023)A Pre-Silicon Detection Based on Deep Learning Model for Hardware TrojansJournal of Circuits, Systems and Computers10.1142/S021812662450144533:08Online publication date: 8-Dec-2023
https://doi.org/10.1142/S0218126624501445
Zhang YDong CXu YYao YLyu C(2022)RLocHT: A Hardware Trojans Localization Method Utilizing Deep Learning at the Gate-Level2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)10.1109/ITAIC54216.2022.9836937(2290-2294)Online publication date: 17-Jun-2022
https://doi.org/10.1109/ITAIC54216.2022.9836937
Dong CXu YLiu XZhang FHe GChen Y(2020)Hardware Trojans in Chips: A Survey for Detection and PreventionSensors10.3390/s2018516520:18(5165)Online publication date: 10-Sep-2020
https://doi.org/10.3390/s20185165

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