Abstract:
In-memory compute kernels present a promising approach for addressing data-centric workloads. However, their scalability—particularly for computationally intensive tasks solving combinatorial optimization problems such as Boolean satisfiability (SAT), which are inherently difficult to decompose—remains a significant challenge. In this work, we propose a ferroelectric nonvolatile memory (NVM)-based compute-in-memory …