A High-Density RRAM-Based Ising Machine with Analog In-Memory Operation for Solving Combinatorial Optimization Problems

This work presents a Resistive RAM (RRAM)-based Ising machine characterized by high spin density and efficient analog in-memory computing. The proposed design enables compact spin representations that support interactions with up to eight neighboring spins at 1-bit precision. The functionality and performance of the proposed Ising machine is evaluated using comprehensive simulation in 28nm CMOS technology, incorporating measured RRAM variation characteristics to validate its robustness and efficiency. Additionally, software-based simulations are employed to solve classical combinatorial problems, such as the Max-Cut problem, while accounting for the non-idealities inherent in the proposed RRAM-based analog in-memory computing approach. Each proposed spin occupies an area of 13.5 μm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, achieving an area reduction of 16.7% to 92.1% compared to recent works, based on feature size normalization.