REMNA: Variation-Resilient and Energy-Efficient MLC FeFET Computing-in-Memory Using NAND Flash-Like Read and Adaptive Control

Nonvolatile memory (NVM)-based computing-in-memory (CiM) has shown promising prospects in deep neural network (DNN) inference at the edge thanks to its nonvolatility and high density. Moreover, most NVMs support multi-level cell (MLC) storage, which can further boost energy efficiency and storage density. However, MLC NVM-based CiMs suffer from degraded accuracy due to device nonidealities, including large variations, nonlinear current distribution, and state drifts. Although prior works have explored various mitigation measures, such as hybrid SLC/MLC, write-and-verify, and local recovery units, the substantial costs from software support, energy, latency, and area still limit the performance. Therefore, the tradeoff between inference accuracy, storage density and compute density has become a vital challenge in NVM-based CiMs.