Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

• URL: http://arxiv.org/abs/2107.01915v2
• Date: Tue, 6 Jul 2021 09:19:20 GMT
• Title: Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities
• Authors: Dominik Sisejkovic, Lennart M. Reimann, Elmira Moussavi, Farhad Merchant, Rainer Leupers
• Abstract summary: This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.
• Score: 0.6287267171078441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.

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