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CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation
arXiv:2606.02781v1 Announce Type: new Abstract: 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.