GenUnlock: An Automated Genetic Algorithm Framework for Unlocking Logic Encryption

Abstract

Logic locking inserts additional key gates to the original circuit for protecting the intellectual property (IP) of modern integrated circuits (ICs). Prior works have identified the vulnerability of logic locking to satisfiability (SAT)-based attacks. However, SAT attacks are ineffective on circuits with SAT-hard structures. In this paper, we propose GenUnlock, the first genetic algorithm-based logic unlocking attack framework addressing the above limitation of SAT attacks. GenUnlock formulates logic unlocking (i.e., identifying the correct keys) as a combinatorial optimization problem and tackles it using genetic algorithms (GAs). Multiple key sequences form the individuals in the population and undergo the following main operations: circuit fitness evaluation, population selection, crossover, and mutation. The key sequences with high fitness scores `survive' the selection and are transformed into the offspring. GenUnlock's evolutionary process of key searching features high scalability, exploration efficiency, and parallelizable fitness evaluation. We take an Algorithm/Software/Hardware co-design approach to optimize GenUnlock's runtime overhead. More specifically, GenUnlock (i) Pipelines each computation stage by automatically constructing auxiliary circuitry for constraints checking, sorting, crossover, and mutation; (ii) Employs hardware emulation on programmable hardware for accelerating circuit fitness evaluation. We perform a comprehensive evaluation of GenUnlock's performance on various benchmarks and demonstrate that GenUnlock achieves up to 1014.1× speedup and is 3974.3× higher energy efficiency compared to the state-of-the-art SAT attacks for logic unlocking.