Solving Boolean satisfiability problems with resistive content addressable memories
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Pedretti, Giacomo Böhm, Fabian Bhattacharya, Tinish Heittman, Arne Zhang, Xiangyi Hizzani, Mohammad Hutchinson, George Kwon, Dongseok Moon, John Valiante, Elisabetta Rozada, Ignacio Graves, Catherine E. Ignowski, Jim Mohseni, Masoud Strachan, John Paul Strukov, Dmitri Beausoleil, Ray Van Vaerenbergh, Thomas |
| author_facet | Pedretti, Giacomo Böhm, Fabian Bhattacharya, Tinish Heittman, Arne Zhang, Xiangyi Hizzani, Mohammad Hutchinson, George Kwon, Dongseok Moon, John Valiante, Elisabetta Rozada, Ignacio Graves, Catherine E. Ignowski, Jim Mohseni, Masoud Strachan, John Paul Strukov, Dmitri Beausoleil, Ray Van Vaerenbergh, Thomas |
| contents | Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional digital architectures, particularly when stochastic decisions are involved. Recently, analog computing architectures for accelerating stochastic optimization solvers have been presented, but they were limited to academic problems in quadratic polynomial format. Here we present KLIMA, a k-Local In-Memory Accelerator with resistive Content Addressable Memories (CAMs) and Dot-Product Engines (DPEs) to accelerate the solution of high-order industry-relevant optimization problems, in particular Boolean Satisfiability. By co-designing the optimization heuristics and circuit architecture we improve the speed and energy to solution up to 182x compared to the digital state of the art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving Boolean satisfiability problems with resistive content addressable memories Pedretti, Giacomo Böhm, Fabian Bhattacharya, Tinish Heittman, Arne Zhang, Xiangyi Hizzani, Mohammad Hutchinson, George Kwon, Dongseok Moon, John Valiante, Elisabetta Rozada, Ignacio Graves, Catherine E. Ignowski, Jim Mohseni, Masoud Strachan, John Paul Strukov, Dmitri Beausoleil, Ray Van Vaerenbergh, Thomas Emerging Technologies Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional digital architectures, particularly when stochastic decisions are involved. Recently, analog computing architectures for accelerating stochastic optimization solvers have been presented, but they were limited to academic problems in quadratic polynomial format. Here we present KLIMA, a k-Local In-Memory Accelerator with resistive Content Addressable Memories (CAMs) and Dot-Product Engines (DPEs) to accelerate the solution of high-order industry-relevant optimization problems, in particular Boolean Satisfiability. By co-designing the optimization heuristics and circuit architecture we improve the speed and energy to solution up to 182x compared to the digital state of the art. |
| title | Solving Boolean satisfiability problems with resistive content addressable memories |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2501.07733 |