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

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.