TAXI: Traveling Salesman Problem Accelerator with X-bar-based Ising Macros Powered by SOT-MRAMs and Hierarchical Clustering

Sangmin Yoo, Kaushik Roy

Abstract: Ising solvers with hierarchical clustering have shown promise for large-scale Traveling Salesman Problems (TSPs), in terms of latency and energy. However, most of these methods still face unacceptable quality degradation as the problem size increases beyond a certain extent. Additionally, their hardware-agnostic adoptions limit their ability to fully exploit available hardware resources. In this work, we introduce TAXI -- an in-memory computing-based TSP accelerator with crossbar(Xbar)-based Ising macros. Each macro independently solves a TSP sub-problem, obtained by hierarchical clustering, without the need for any off-macro data movement, leading to massive parallelism. Within the macro, Spin-Orbit-Torque (SOT) devices serve as compact energy-efficient random number generators enabling rapid "natural annealing". By leveraging hardware-algorithm co-design, TAXI offers improvements in solution quality, speed, and energy-efficiency on TSPs up to 85,900 cities (the largest TSPLIB instance). TAXI produces solutions that are only 22% and 20% longer than the Concorde solver's exact solution on 33,810 and 85,900 city TSPs, respectively. TAXI outperforms a current state-of-the-art clustering-based Ising solver, being 8x faster on average across 20 benchmark problems from TSPLib.