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

TL;DR

This survey reviews recent advances in logic locking for integrated circuits, emphasizing how machine learning techniques are used to evaluate and attack these schemes, and discusses future research directions.

Contribution

It provides a comprehensive overview of the intersection between logic locking and machine learning, highlighting recent attack methods, countermeasures, and future opportunities.

Findings

Machine learning enables new evaluation methods for logic locking.

Recent attacks leverage deep learning models to break logic locking schemes.

Opportunities exist for designing more robust logic locking techniques.

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.