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Abstract With the slowdown of improvement in conventional von Neumann systems, increasing attention is paid to novel paradigms such as Ising machines. They have very different approach to solving combinatorial optimization problems. Ising machines have shown great potential in solving binary optimization problems like MaxCut. In this paper, we present an analysis of these systems in boolean satisfiability (SAT) problems. We demonstrate that, in the case of 3-SAT, a basic architecture fails to produce meaningful acceleration, largely due to the relentless progress made in conventional SAT solvers. Nevertheless, careful analysis attributes part of the failure to the lack of two important components: cubic interactions and efficient randomization heuristics. To overcome these limitations, we add proper architectural support for cubic interaction on a state-of-the-art Ising machine. More importantly, we propose a novel semantic-aware annealing schedule that makes the search-space navigation much more efficient than existing annealing heuristics. Using numerical simulations, we show that such an “Augmented” Ising Machine for SAT is projected to outperform state-of-the-art software-based, GPU-based and conventional hardware SAT solvers by orders of magnitude. 

Analog Ising machines (IMs) occupy an increasingly prominent area of computer architecture research, offering high-quality, low-latency, and low-energy solutions to intractable computing tasks; however, IMs have a fixed capacity, with little to no utility in out-of-capacity problems. Previous works have proposed parallel, multi-IM architectures to circumvent this limitation [A. Sharma, , in , ISCA ’22 (Association for Computing Machinery, New York, NY, USA, 2022), p. 508; R. Santos, , Enhancing quantum annealing via entanglement distribution, ArXiv:2212.02465]. In this work, we theoretically and numerically investigate trade-offs in parallel IM networks to guide researchers in this burgeoning field. We propose formal models of parallel IM execution models, and we then provide theoretical guarantees for probabilistic convergence. Numerical experiments illustrate our findings and provide empirical insights into the high- and low-synchronization-frequency regimes. We also provide practical heuristics for parameter and model selection, informed by our theoretical and numerical findings. 

Dynamical solvers for combinatorial optimization are usually based on 2superscript{nd} degree polynomial interactions, such as the Ising model. These exhibit high success for problems that map naturally to their formulation. However, SAT requires higher degree of interactions. As such, these quadratic dynamical solvers (QDS) have shown poor solution quality due to excessive auxiliary variables and the resulting increase in search-space complexity. Thus recently, a series of cubic dynamical solver (CDS) models have been proposed for SAT and other problems. We show that such problem-agnostic CDS models still perform poorly on moderate to large problems, thus motivating the need to utilize SAT-specific heuristics. With this insight, our contributions can be summarized into three points. First, we demonstrate that existing make-only heuristics perform poorly on scale-free, industrial-like problems when integrated into CDS. This motivates us to utilize break counts as well. Second, we derive a relationship between make/break and the CDS formulation to efficiently recover break counts. Finally, we utilize this relationship to propose a new make/break heuristic and combine it with a state-of-the-art CDS which is projected to solve SAT problems several orders of magnitude faster than existing software solvers.