Implicitly Parallel Neuromorphic Solver Design for Constraint Satisfaction Problems

Many real-life problems of practical importance -- spanning a wide range of applications from chip design to bioinformatics -- represent constraint satisfaction problems, where classical solvers have to rely on heuristic approximations due to the computational complexity. Neuromorphic solvers, on the other hand, offer a unique alternative representation which enables an inherently parallel exploration of the solution space. This paper provides a theoretical characterization and experimental demonstration of this native type of parallelism that is hard to apply to classical solvers. We observe that more than two orders of magnitude faster operation is possible without compromising solution accuracy. Our study represents the first step toward bridging the theory vs. practice gap to unlock the performance potential of emerging neuromorphic solvers.