Solving Quadratic Unconstrained Binary Optimization with Collaborative Spiking Neural Networks

TL;DR: In this article , a novel neuromorphic computing paradigm that employs multiple collaborative spiking neural networks to solve quadratic unconstrained binary optimization (QUBO) problems is proposed, where each SNN conducts a local stochastic gradient descent search and shares the global best solutions periodically to perform a metaheuristic search for optima.

Abstract: Quadratic Unconstrained Binary Optimization (QUBO) problem becomes an attractive and valuable optimization problem formulation in that it can easily transform into a variety of other combinatorial optimization problems such as Graph/number Partition, Max-Cut, SAT, Vertex Coloring, TSP, etc. Some of these problems are NP-hard and widely applied in industry and scientific research. Meanwhile, QUBO has been discovered to be compatible with two emerging computing paradigms, neuromorphic computing, and quantum computing, with tremendous potential to speed up future optimization solvers. In this paper, we propose a novel neuromorphic computing paradigm that employs multiple collaborative spiking neural networks to solve QUBO problems. Each SNN conducts a local stochastic gradient descent search and shares the global best solutions periodically to perform a meta-heuristic search for optima. We simulate our model and compare it to a single SNN solver and a mult-SNN solver without collaboration. Through tests on benchmark problems, the proposed method is demonstrated to be more efficient and effective in searching for QUBO optima. Specifically, it exhibits x10 and x15-20 speedup respectively on the multi-SNN solver without collaboration and the single-SNN solver.