A Stochastic Neural Model for Graph Problems: Software and Hardware Implementation

This article describes a novel neural stochastic model for solving graph problems. The neural system has been tested on random graphs, showing better performance than other well-known heuristics for the same problems. Furthermore, a simplified version of the proposed model has been developed in such a way that it can be easily implemented in hardware using programmable logic chips, such as FPGAs