3D Memristor Crossbar Architecture for a Multicore Neuromorphic System

This is the authors' abstract. We don't add key points for this paper.

Compact online learning architectures can be used to enhance internet of things devices, allowing them to learn directly on received data instead of sending data to a remote server for learning. This saves communication energy and enhances privacy and security, as the data is not shared. This paper presents a 3D memristor based multicore architecture capable of on-line learning, where a mixed signal design utilizes memristor crossbars to both store synaptic weights and to carry out analog dot product computations. A 3D memristor architecture is one where several memristor crossbars are stacked vertically on top of each other to reduce the area footprint of a chip. A variety of deep learning applications can be processed using the proposed architecture, and we show how the simulated architecture is able to learn and classify several datasets. In this work we also examine how changing the number of vertically stacked crossbars within a neural core impacts energy, time, and area requirements of the architecture.