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启发的、与内存计算兼容的并行框架，该框架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获得超越单纯并行化所能实现的性能提升，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适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。由此产生的受并行回火启发的协同WalkSAT算法在76.3%的测试问题实例上，其求解所需迭代次数优于传统WalkSAT启发式算法；其朴素并行变体在68.4%的实例上优于传统算法。针对两种硬件加速器架构的PTIC框架能耗开销估算表明，在两种情况下运行该框架的开销均低于运行各加速器所需总能耗的1%。