Dataflow-Preserving, Overhead-Free Weight Remapping for Fault-Tolerant ReRAM-Based In-Memory Computing
Resistive random-access memory (ReRAM)-based in-memory computing (IMC) systems provide high energy efficiency and storage density for deep neural network (DNN) acceleration, but stuck-at faults (SAFs) substantially degrade reliability. Weight remapping (WR) can mitigate SAFs; however, existing approaches either ignore dataflow consistency or require additional hardware to restore it. We propose FREEMAP, an overhead-free WR algorithm that preserves dataflow consistency without runtime operations or hardware modifications. FREEMAP combines layer-wise filter reordering (LFR), which globally reorders filters across a layer for fault resilience, with row group remapping (RGR), which realigns the next layer's weight rows to the reordered outputs. Across diverse models and datasets, FREEMAP eliminates the hardware overhead of conventional WR, reducing area and energy to 0.02×-0.06× and 0.04×-0.14×, respectively. Compared with state-of-the-art dataflow-aware WR, it further reduces area and energy to 0.45×-0.47× and 0.52×-0.63× while maintaining comparable accuracy.