# Temporal correlation detection using computational phase-change memory

## TL;DR

This paper demonstrates large-scale use of phase-change memory devices to perform computational tasks within memory, enabling ultra-dense, low-power, and parallel computing architectures by exploiting nanoscale physics.

## Contribution

It introduces a novel large-scale experimental system of one million phase-change memory devices for in-memory computation using their crystallization dynamics.

## Key findings

- Successful large-scale experimental demonstration with one million devices
- Effective processing of real-world data-sets using computational memory
- Potential for ultra-dense, low-power, massively parallel computing systems

## Abstract

For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes increasingly data-centric and as the scalability limits in terms of performance and power are being reached, alternative computing paradigms are searched for in which computation and storage are collocated. A fascinating new approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. Here we present a large-scale experimental demonstration using one million phase-change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Also presented is an application of such a computational memory to process real-world data-sets. The results show that this co-existence of computation and storage at the nanometer scale could be the enabler for new, ultra-dense, low power, and massively parallel computing systems.