Demonstration of 4-quadrant analog in-memory matrix multiplication in a single modulation.

Analog in-memory computing (AIMC) leverages the inherent physical characteristics of resistive memory devices to execute computational operations, notably matrix-vector multiplications (MVMs). However, executing MVMs using a single-phase reading scheme to reduce latency necessitates the simultaneous application of both positive and negative voltages across resistive memory devices. This degrades the accuracy of the computation due to the dependence of the device conductance on the voltage polarity.

Publisher Correction: Memory devices and applications for in-memory computing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mixed-Precision Deep Learning Based on Computational Memory.

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance states and perform the expensive weighted summations in place in a non-von Neumann manner.

Memory devices and applications for in-memory computing.

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing.