In-Memory Approximate Computing Architecture Based on 3D-NAND Flash Memories

Abstract

A high performance 3D-NAND-flash based approximate computing architecture is proposed to execute in-memory similarity computation. This approximate-computing chip features fuzzy in-memory search (IMS) function with ultra-high parallelism at full-block scale in just one read cycle. The system architecture from the IMS unit cell/string/array configuration to the novel approximate comparison scheme are discussed in detail. Practical issues including Vt distribution, retention loss, and read disturbance are evaluated. We also introduce a novel IMS group-encoding scheme, which can significantly increase the content density under the same string length. Face recognition with VGGFace2 dataset is demonstrated with high accuracy and good tolerability on reliability degradation.