Special Issue Papers

An Area-Efficient Ising Machine Combining Parallel Tempering and Stochastic Cellular Automata

  • YANG ZHANG ,
  • LONGYUAN KANG ,
  • XIANGRUI WANG ,
  • ENYI YAO
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  • School of Microelectronics, South China University of Technology, Guangzhou 511442, China
ENYI YAO (e-mail: ).

ENYI YAO (Member, IEEE)

Received date: 2024-12-31

  Revised date: 2025-02-27

  Accepted date: 2025-03-16

  Online published: 2025-10-22

Supported by

Guangdong Basic and Applied Basic Research Foundation under Grant(2022A1515110045)

Guangdong Basic and Applied Basic Research Foundation under Grant(2023A1515011241)

GJYC program of Guangzhou under Grant(2024D03J0006)

Abstract

As a potential solver for combinatorial optimization problems (COPs), the convergence speed and accuracy of Ising machines still have room to be improved at the level of algorithm and architecture design. In this paper, a novel parallel stochastic cellular automata tempering (PSCAT) algorithm is proposed, which combines the high parallel efficiency of stochastic cellular automata annealing (SCA) with the more balanced Monte Carlo sampling of parallel tempering (PT), to enhance the performance of fully-connected Ising machines. To achieve an area-efficient hardware design, a modified temperature exchange probability is applied to reduce the number of replicas and the utilization of the spin update module is improved by reducing the flip decision block. Additionally, the proposed coefficient access pattern effectively reduces memory overhead by sharing the weight matrix. The design prototype with 2,048 spins and 8 replicas is validated on FPGA. Using the K2000 max-cut problem as a benchmark, our design achieves a solution accuracy of 98.94% within 0.5 ms, which is higher than two state-of-the-art works.

Cite this article

YANG ZHANG , LONGYUAN KANG , XIANGRUI WANG , ENYI YAO . An Area-Efficient Ising Machine Combining Parallel Tempering and Stochastic Cellular Automata[J]. Integrated Circuits and Systems, 2025 , 2(1) : 22 -27 . DOI: 10.23919/ICS.2025.3553464

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