Solving Boolean satisfiability problems with resistive content addressable memories

TL;DR

KLIMA leverages resistive CAMs and DPEs to significantly accelerate solving high-order Boolean satisfiability problems, achieving up to 182x speed and energy efficiency improvements over digital methods.

Contribution

This work introduces KLIMA, a novel in-memory analog accelerator that co-designs heuristics and architecture for efficient high-order optimization problem solving.

Findings

Achieves up to 182x speedup over digital solutions.

Demonstrates energy efficiency improvements in solving Boolean satisfiability.

Successfully accelerates industry-relevant high-order optimization problems.

Abstract

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.