Fast Solving Complete 2000-Node Optimization Using Stochastic-Computing Simulated Annealing

TL;DR

This paper introduces a stochastic-computing based simulated annealing method that significantly accelerates large-scale combinatorial optimization, achieving faster convergence and better solutions than traditional approaches.

Contribution

The paper presents a novel stochastic computing implementation of simulated annealing that outperforms existing methods in speed and solution quality for large MAX-CUT problems.

Findings

SC-SA is several orders of magnitude faster than traditional SA.

SC-SA achieves better MAX-CUT scores on K2000 benchmark.

Experimental results demonstrate improved efficiency and solution quality.

Abstract

In this paper, we evaluate stochastic-computing simulated annealing (SC-SA) for solving large-scale combinatorial optimization problems. SC-SA is designed using stochastic computing, where the computatoin is reazlied using random bitstream, resulting in fast converging to the global minimum energy of the problems. The proposed SC-SA is compared with a typical SA and existing simulated-annealing (SA) processors on the maximum cut (MAX-CUT) problems, such as Gset that is a benchmark for SA. The simulation results show that SC-SA realizes a few orders of magnitude faster than a typical SA. In addition, SC-SA achieves better MAX-CUT scores than other existing methods on K2000 that is a complete 2000-node optimization problem.