Computer Science > Emerging Technologies
[Submitted on 13 Sep 2024 (v1), last revised 3 Dec 2024 (this version, v3)]
Title:Distributed Binary Optimization with In-Memory Computing: An Application for the SAT Problem
View PDF HTML (experimental)Abstract: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 naïve 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.
Submission history
From: Ignacio Rozada [view email][v1] Fri, 13 Sep 2024 19:14:48 UTC (6,967 KB)
[v2] Wed, 6 Nov 2024 17:28:37 UTC (6,933 KB)
[v3] Tue, 3 Dec 2024 21:56:33 UTC (6,933 KB)
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