Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array

TL;DR

This paper introduces a scalable, massively parallel computing method using continuous-time data representation in nanoscale crossbar arrays, enabling real-time processing of analogue data for IoT applications.

Contribution

It presents a novel approach combining continuous data representation and frequency multiplexing in nanoscale crossbar arrays for parallel analogue computing.

Findings

Enables parallel reading and one-shot matrix multiplications in crossbar arrays.

Demonstrates recognition of 16 letter images using interconnected arrays.

Shows simultaneous processing and modulation of analogue information.

Abstract

The growth of connected intelligent devices in the Internet of Things has created a pressing need for real-time processing and understanding of large volumes of analogue data. The difficulty in boosting the computing speed renders digital computing unable to meet the demand for processing analogue information that is intrinsically continuous in magnitude and time. By utilizing a continuous data representation in a nanoscale crossbar array, parallel computing can be implemented for the direct processing of analogue information in real time. Here, we propose a scalable massively parallel computing scheme by exploiting a continuous-time data representation and frequency multiplexing in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix-matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar arrays and demonstrate that the processing and modulation of analogue information can be simultaneously performed in a memristive crossbar array.