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.
@article{arxiv.2501.07733,
title = {Solving Boolean satisfiability problems with resistive content addressable memories},
author = {Giacomo Pedretti and Fabian Böhm and Tinish Bhattacharya and Arne Heittman and Xiangyi Zhang and Mohammad Hizzani and George Hutchinson and Dongseok Kwon and John Moon and Elisabetta Valiante and Ignacio Rozada and Catherine E. Graves and Jim Ignowski and Masoud Mohseni and John Paul Strachan and Dmitri Strukov and Ray Beausoleil and Thomas Van Vaerenbergh},
journal= {arXiv preprint arXiv:2501.07733},
year = {2025}
}