Equivalence Checking

Equivalence checking verifies whether two systems, such as different versions of a program or neural network, produce identical outputs for all inputs. Current research focuses on improving the efficiency and scalability of equivalence checking, particularly for complex systems like Boolean circuits and neural networks, employing techniques like SAT solvers, graph neural networks, and reinforcement learning with compiler feedback. These advancements are crucial for ensuring the reliability and correctness of software and hardware systems, particularly in safety-critical applications, and for optimizing resource-intensive machine learning models.