受并行回火启发的存内计算分布式二进制优化
Parallel Tempering-Inspired Distributed Binary Optimization with In-Memory Computing
AI总结：
本研究提出一种兼容存内计算的并行回火启发并行框架，经布尔可满足性问题验证，该框架可在极低硬件能耗开销下显著提升存内计算求解器的优化性能与成功率。
摘要：
存内计算（IMC）已被证明是求解二进制优化问题的一种极具潜力的方法，可显著降低能耗与延迟。依托并行计算的优势，我们提出了一种兼容IMC的并行框架，该框架基于受物理学启发的并行回火（PT）算法，支持跨副本通信，以提升IMC求解器的性能。该框架不仅能让IMC求解器的性能超越单纯并行化所能达到的水平，还能以较低的硬件开销为搜索过程提供更高的灵活性。我们论证了该框架几乎可应用于所有IMC求解器。我们以布尔可满足性（SAT）问题为例验证了该框架的有效性，采用WalkSAT启发式算法作为现有IMC求解器的代理。由此得到的受PT启发的协同WalkSAT（PTIC-WalkSAT）算法，在84.0%的测试问题实例中，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（PA-WalkSAT）则在64.9%的实例中表现更优，且在大多数实例中成功率更高。针对两种硬件加速器架构对PTIC框架的能耗开销进行估算，结果表明，在两种架构下，运行PTIC框架的开销均低于运行各加速器所需总能耗的1%。
英文摘要：
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 naïve 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.
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