Fan-In Aware Graph-Based Optimization for MAC-Based In-Memory Computing

Resistive RAM (RRAM) has emerged as a promising technology for in-memory computing, allowing both storage and computation within the same physical substrate. Although its ability to perform analog computations, especially multiplyaccumulate (MAC) operations, has been effectively utilized in neuromorphic systems, there has been limited research on its applicability to Boolean logic synthesis. Existing approaches typically rely on graph-based representations of Boolean functions that are mapped to column-wise MAC operations on standard RRAM crossbars. However, these representations largely inherit binary fan-in constraints from conventional logic synthesis flows, resulting in limited exploitation of MAC-level parallelism and underutilization of available crossbar resources. In this work, we address this limitation by introducing the concept of multi-input OR-Inverter Graphs (m-OIGs), which allow OR nodes with fanin greater than two to better match the accumulation semantics of MAC operations. Experimental results on standard benchmark suites demonstrate that increasing OR fan-in consistently reduces both crossbar area and total evaluation cycles, leading to improved performance and more efficient use of RRAM crossbar resources, highlighting the importance of fan-in-aware logic representations.