Noise and Drift Models for Protonic ECRAM and Their Impact on Analog In‐Memory Computing
Analog in‐memory computing (AIMC) with nonvolatile memories (NVMs) offers a promising route toward energy‐efficient deep learning. However, because AIMC relies on analog device states, understanding device‐level nonidealities, particularly noise and drift, is essential for assessing achievable computing performance....