CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation

ArXi:2606.02781v1 Announce Type: cross Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains. However, typical Von Neumann compute paradigms face severe memory bottlenecks. Emerging near-memory and compute-in-memory approaches alleviate this but incur significant peripheral overhead. Computational Random Access Memory (CRAM) based on MRAM enables in-situ logic without peripheral overhead, offering a dense, energy-efficient solution.