In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advantages of parallel computation, we propose an IMC-compatible parallelism framework based on the physics-inspired parallel tempering (PT) algorithm, enabling cross-replica communication to improve the performance of IMC solvers. This framework enables an IMC solver not only to improve performance beyond what can be achieved through parallelization, but also affords greater flexibility for the search process with low hardware overhead. We justify that the framework can be applied to almost any IMC solver. We demonstrate the effectiveness of the framework for the Boolean satisfiability (SAT) problem, using the WalkSAT heuristic as a proxy for existing IMC solvers. The resulting PT-inspired cooperative WalkSAT (PTIC-WalkSAT) algorithm outperforms the standard WalkSAT heuristic in terms of the iterations-to-solution in 84.0% of the tested problem instances and its na\"ive parallel variant (PA-WalkSAT) does so in 64.9% of the instances, and with a higher success rate in the majority of instances. An estimate of the energy overhead of the PTIC framework for two hardware accelerator architectures indicates that in both cases the overhead of running the PTIC framework would be less than 1% of the total energy required to run each accelerator.

翻译：内存计算已被证明是解决二进制优化问题的一种有前景的方法，同时能显著降低能耗和延迟。基于并行计算的优势，我们提出了一种与内存计算兼容的并行化框架，该框架基于受物理学启发的并行回火算法，支持副本间通信以提升内存计算求解器的性能。该框架不仅使内存计算求解器能够超越单纯并行化所能达到的性能水平，还为搜索过程提供了更大的灵活性，且硬件开销较低。我们论证了该框架可应用于几乎任何内存计算求解器。我们以布尔可满足性问题为例，使用 WalkSAT 启发式算法作为现有内存计算求解器的代理，验证了该框架的有效性。由此得到的并行回火启发的协作式 WalkSAT 算法在 84.0% 的测试问题实例上，其求解所需迭代次数优于标准 WalkSAT 启发式算法；其朴素并行变体在 64.9% 的实例上优于标准算法，且在大多数实例中具有更高的成功率。针对两种硬件加速器架构对 PTIC 框架能量开销的估算表明，在两种情况下，运行 PTIC 框架的开销均低于运行各加速器所需总能量的 1%。