A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing

Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is required to deal with circuit mismatches and non-idealities associated with the memory devices. Efficient near-memory digital logic is critical to retain the high area/energy efficiency and low latency of AIMC. Existing systems adopt Floating Point 16 (FP16) arithmetic with limited parallelization capability and high latency. To overcome these limitations, we propose a Near-Memory digital Processing Unit …

analog computing cs.ar cs.et cs.lg deal deep learning devices digital efficiency emerging technology energy energy efficiency fixed-point inference in-memory in-memory computing logic memory near post-processing precision processing technology