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GPU-Accelerated Simulated Oscillator Ising/Potts Machine Solving Combinatorial Optimization Problems
Conference proceeding   Open access

GPU-Accelerated Simulated Oscillator Ising/Potts Machine Solving Combinatorial Optimization Problems

Yilmaz Ege Gonul, Ceyhun Efe Kayan, Ilknur Mustafazade and Baris Taskin
GLSVLSI '25: Proceedings of the Great Lakes Symposium on VLSI 2025, pp 112-117
29 Jun 2025
DOI: https://doi.org/10.1145/3716368.3735247
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1145/3716368.3735247View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0,  Open

Abstract

Oscillator-based Ising machines (OIMs) and oscillator-based Potts machines (OPMs) have emerged as promising hardware accelerators for solving NP-hard combinatorial optimization problems by leveraging the phase dynamics of coupled oscillators. In this work, a GPU-accelerated simulated OIM/OPM digital computation framework capable of solving combinatorial optimization problems is presented. The proposed implementation harnesses the parallel processing capabilities of GPUs to simulate large-scale OIM/OPMs, leveraging the advantages of digital computing to offer high precision, programmability, and scalability. The performance of the proposed GPU framework is evaluated on the max-cut problems from the GSET benchmark dataset and graph coloring problems from the SATLIB benchmarks dataset, demonstrating competitive speed and accuracy in tackling large-scale problems. The results from simulations, reaching up to 11295 × speed-up over CPUs with up to 99% accuracy, establish this framework as a scalable, massively parallelized, and high-fidelity digital realization of OIM/OPMs.

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