Neurophysics-inspired parallel architecture with resistive crosspoint array for dictionary learning

This paper proposes a parallel architecture with resistive crosspoint array. The design of its two essential operations, Read and Write, is inspired by the biophysical behavior of a neural system, such as integrate-and-fire and time-dependent synaptic plasticity. The proposed hardware consists of an array with resistive random access memory (RRAM) and CMOS peripheral circuits, which perform matrix product and dictionary update in a fully parallel fashion, at the speed that is independent of the matrix dimension. The entire system is implemented in 65nm CMOS technology with RRAM to realize high-speed unsupervised dictionary learning. As compared to state-of-the-art software approach, it achieves more than 3000X speedup, enabling real-time feature extraction on a single chip.