FIMA: A Scalable Ferroelectric Compute-in-Memory Annealer for Accelerating Boolean Satisfiability
Mohammad Khairul Bashar, Tanvir Haider Pantha, Z. Li, Malihe Farasat, Suman Datta, Vijaykrishnan Narayanan, Sourav Dutta, Nikhil Shukla · IEEE Journal on Exploratory Solid-State Computational Devices and Circuits · 2025
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 non-volatile memory (NVM)-based compute-in-memory annealer for solving the Boolean MaxSAT problem. We experimentally demonstrate the computational functionality of the NVM array using a compact 20×10 HZO/IWO-based FeFET array. More importantly, through experimentally calibrated simulations, we demonstrate that our solution is compatible with a modular memory architecture, allowing the problem sizes to exceed the capacity of a single memory array. Our approach not only addresses the size limitations imposed by the read margin of individual arrays but also opens new avenues for integrating such accelerators as back-end solutions in advanced computing platforms.