Parallel tempering–inspired distributed binary optimization with in-memory computing
Improving the performance of parallel computing usually comes at the cost of complexity and high energy consumption. This study combines multiple in-memory computing solvers via a parallel-tempering framework, and shows an increase in the speed and energy efficiency of solving binary optimization problems with negligible energy overhead. This approach is expected to have an impact on engineering solutions to Boolean satisfiability problems, Ising machines, and other binary optimization problems with applications in fields such as circuit design and supply-chain management, among others.
Authors
- E. Valiante (ORCID: https://orcid.org/0000-0003-3820-7851)
- Moslem Noori (ORCID: https://orcid.org/0000-0002-7524-4240)
- Thomas Van Vaerenbergh (ORCID: https://orcid.org/0000-0002-7301-8610)
- Fabian Böhm (ORCID: https://orcid.org/0000-0001-8516-9785)
- Ignacio Rozada
- Raymond G. Beausoleil (ORCID: https://orcid.org/0000-0003-1139-1660)
- Xiangyi Zhang (ORCID: https://orcid.org/0000-0003-4395-2776)
- Masoud Mohseni (ORCID: https://orcid.org/0000-0003-4065-9400)
- Giacomo Pedretti (ORCID: https://orcid.org/0000-0002-4501-8672)
- Chuhong Yang
Institutions
- Hewlett Packard Enterprise (United States) (US)
- Saskatchewan Health Quality Council (CA)
Publication Details
- Journal
- Physical Review Applied
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1103/physrevapplied.23.034031
- Citations
- 2
- Primary Topic
- Graph Theory and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 2.55
Funders
- Defense Advanced Research Projects Agency