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 inspired by parallel tempering (PT), 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 traditional WalkSAT heuristic in terms of the iterations-to-solution in 76.3% of the tested problem instances and its na\"ive parallel variant (PA-WalkSAT) does so in 68.4% of the 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（IMC）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，我们提出了一种受并行回火（PT）启发的IMC兼容并行框架，该框架支持副本间通信以提升IMC求解器的性能。此框架不仅使IMC求解器能够突破单纯并行化所能达到的性能极限，还能以较低的硬件开销为搜索过程提供更大灵活性。我们论证了该框架可适用于几乎任何IMC求解器。通过以WalkSAT启发式算法作为现有IMC求解器的代理，我们展示了该框架在布尔可满足性（SAT）问题上的有效性。由此产生的PT启发协作式WalkSAT（PTIC-WalkSAT）算法在76.3%的测试问题实例中，其求解所需迭代次数优于传统WalkSAT启发式算法；在68.4%的实例中优于其朴素并行变体（PA-WalkSAT）。针对两种硬件加速器架构的PTIC框架能耗开销估算表明，在两种情况下运行PTIC框架的开销均低于各加速器总运行能耗的1%。