🤖 AI Summary
This work addresses the challenge of automatically mapping and efficiently solving combinatorial optimization problems on probabilistic computing architectures. It proposes an Ising-model-based automated mapping approach that translates optimization problems into hardware-compatible formulations by constructing corresponding Hamiltonians and configuring the number of p-bits. A key innovation is the introduction of an adaptive algorithm selection mechanism that dynamically switches among Gibbs sampling, simulated annealing, simulated quantum annealing, and cluster updates to significantly enhance convergence speed and robustness. Validated through MTJ-based hardware modeling on standard benchmark instances, the proposed framework demonstrates superior performance over fixed-strategy approaches, offering a scalable and systematic co-design and evaluation paradigm for probabilistic computing systems.
📝 Abstract
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.