Implicitly Parallel Neuromorphic Solver Design for Constraint Satisfaction Problems

TL;DR

This paper explores neuromorphic solvers for constraint satisfaction problems, demonstrating they can operate over two orders of magnitude faster than classical methods by leveraging inherent parallelism, with theoretical and experimental validation.

Contribution

It provides the first theoretical and experimental analysis of the native parallelism in neuromorphic solvers for constraint satisfaction problems.

Findings

Over two orders of magnitude faster operation possible

Native parallelism enables efficient exploration of solution space

No compromise on solution accuracy

Abstract

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.