A Tool for the Synthesis of Adaptive Probabilistic Processors Based on the Ising Model
For researchers and engineers working on probabilistic computing hardware, this tool provides a systematic framework for evaluating and developing adaptive strategies, though the improvements are incremental.
This work presents a tool for synthesizing and simulating probabilistic architectures for combinatorial optimization problems using the Ising model, which automatically constructs the Hamiltonian and adaptively selects update algorithms. Experimental results show improved convergence behavior and flexibility compared to fixed approaches.
This work presents a tool for the synthesis and simulation of probabilistic architectures for solving combinatorial optimization problems by mapping them to the Ising model. The proposed approach automatically constructs the Ising Hamiltonian and determines the number of probabilistic elements (p-bits) based on problem characteristics such as size and topology. Furthermore, the tool introduces an adaptive strategy for selecting the most suitable update algorithm among Gibbs Sampling, Simulated Annealing (SA), Simulated Quantum Annealing (SQA), and cluster-based methods. Experimental results using benchmark problems demonstrate improved convergence behavior and flexibility compared to fixed approaches. The proposed framework enables systematic evaluation of probabilistic computing strategies and supports the development of future hardware implementations based on MTJs and p-bits.