Distributed Binary Optimization with In-Memory Computing: An Application for the SAT Problem
Tallennettuna:
| Julkaisussa: | arXiv.org (Dec 3, 2024), p. n/a |
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| Päätekijä: | |
| Muut tekijät: | , , , , , , , , |
| Julkaistu: |
Cornell University Library, arXiv.org
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| Aiheet: | |
| Linkit: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3106245815 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3106245815 | ||
| 045 | 0 | |b d20241203 | |
| 100 | 1 | |a Zhang, Xiangyi | |
| 245 | 1 | |a Distributed Binary Optimization with In-Memory Computing: An Application for the SAT Problem | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 3, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Search process | ||
| 653 | |a Heuristic | ||
| 653 | |a Parallel processing | ||
| 653 | |a Solvers | ||
| 653 | |a Algorithms | ||
| 653 | |a Performance enhancement | ||
| 653 | |a Computation | ||
| 653 | |a Hardware | ||
| 653 | |a Distributed memory | ||
| 653 | |a Optimization | ||
| 700 | 1 | |a Böhm, Fabian | |
| 700 | 1 | |a Valiante, Elisabetta | |
| 700 | 1 | |a Noori, Moslem | |
| 700 | 1 | |a Thomas Van Vaerenbergh | |
| 700 | 1 | |a Chan-Woo, Yang | |
| 700 | 1 | |a Pedretti, Giacomo | |
| 700 | 1 | |a Mohseni, Masoud | |
| 700 | 1 | |a Beausoleil, Raymond | |
| 700 | 1 | |a Rozada, Ignacio | |
| 773 | 0 | |t arXiv.org |g (Dec 3, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3106245815/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2409.09152 |