A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation

Author:

Koblah David1ORCID,Acharya Rabin1ORCID,Capecci Daniel1ORCID,Dizon-Paradis Olivia1ORCID,Tajik Shahin2ORCID,Ganji Fatemeh2ORCID,Woodard Damon1ORCID,Forte Domenic1ORCID

Affiliation:

1. University of Florida, Gainesville, FL

2. Worcester Polytechnic Institute, Worcester, MA

Abstract

Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in several fields to improve performance and the level of automation. In recent years, this use has exponentially increased due to the advancement of high-performance computing and the ever increasing size of data. One of such fields is that of hardware design—specifically the design of digital and analog integrated circuits, where AI/ ML techniques have been extensively used to address ever-increasing design complexity, aggressive time to market, and the growing number of ubiquitous interconnected devices. However, the security concerns and issues related to integrated circuit design have been highly overlooked. In this article, we summarize the state-of-the-art in AI/ML for circuit design/optimization, security and engineering challenges, research in security-aware computer-aided design/electronic design automation, and future research directions and needs for using AI/ML for security-aware circuit design.

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference297 articles.

1. Victoria Fraza Kickham. 2012. Top 5 Most Counterfeited Parts Represent a 169 Billion Potential Challenge for Global Semiconductor Market. Retrieved September 20 2022 from https://www.electronicdesign.com/21194728.

2. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado et al. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved September 20 2022 from https://www.tensorflow.org/.

3. Rabin Yu Acharya, Sreeja Chowdhury, Fatemeh Ganji, and Domenic Forte. 2020. Attack of the genes: Finding keys and parameters of locked analog ICs using genetic algorithm. In Proceedings of the 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST’20). IEEE, Los Alamitos, CA, 284–294.

4. Rabin Yu Acharya, Michael Valentin Levin, and Domenic Forte. 2021. LDO-based odometer to combat IC recycling. In Proceedings of the 2021 IEEE 34th International System-on-Chip Conference (SOCC’21). IEEE, Los Alamitos, CA, 206–211.

5. Ali Ahmadi, Mohammad-Mahdi Bidmeshki, Amit Nahar, Bob Orr, Michael Pas, and Yiorgos Makris. 2016. A machine learning approach to fab-of-origin attestation. In Proceedings of the 35th International Conference on Computer-Aided Design. 1–6.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques;Internet of Things. Advances in Information and Communication Technology;2023-10-26

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3