DCAP: A Scalable Decoupled-Clustering Annealing Processor for Large-Scale Traveling Salesman Problems

Abstract

The Traveling Salesman Problem (TSP) is one of the most well-known NP-hard combinatorial optimization problems (COPs). Many social production problems can be effectively represented as instances of TSPs. However, solving large-scale TSPs remains a significant challenge for conventional Von Neumann computers. Many studies have proposed annealing processors to address large-scale COPs, but most of them focus on unconstrained problems, such as the Maxcut problem. In this paper, a scalable decoupled-clustering annealng processor (DCAP) for efficiently handling large-scale TSPs is presented. A decoupled hierarchical clustering algorithm is proposed for higher convergence speed and improved scalability. Several techniques have been developed in hardware to minimize area overhead and processing time, including a modified spin connection topology for the Ising model, an area-efficient random threshold generator, a one-step spin update scheme and a dynamic prediction method. The DCAP prototype is implemented on FPGA with an operating frequency of 125MHz. We tested our design on various TSP instances from the TSPLIB. Results show that our design outperforms the CPU- and GPU-based Neuro-Ising scheme by achieving maximum speedups of $780\times $ and a 42% improvement in accuracy. With multi-chip interconnection, DCAP is able to handle problems of scale up to 85900 cities.