The escalating computational demands of large-scale machine learning models have highlighted the fundamental performance limits of traditional computing architectures, where data must constantly shuttle between processing units and memory blocks. This continuous data transfer creates significant energy loss and latency delays, often termed the von Neumann bottleneck. Resistive random-access...