Temporal correlation detection using computational phase-change memory

Abu Sebastian,Nikolaos Papandreou,Tomas Tuma,Manuel Le Gallo,Evangelos Eleftheriou,Lukas Kull,Thomas Parnell

doi:10.1038/s41467-017-01481-9

Abstract

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such 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. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.

Highlights

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units
The memory unit typically comprises dynamic random-access memory (DRAM), where information is stored in the charge state of a capacitor
Performing an operation, f, over a set of data stored in the memory, A, to obtain the result, f(A), requires a sequence of steps in which the data must be obtained from the memory, transferred to the processing unit, processed, and stored back to the memory

Summary

Introduction

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. A tantalizing prospect is that of transitioning to a hybrid architecture where certain operations, such as f, can be performed at the same physical location as where the data is stored (Fig. 1b) Such a memory unit that facilitates collocated computation is referred to as computational memory. Hardware accelerators based on this concept are becoming an important subject of research[11,12,13,14,15,16,17] In these applications, the cross-bar array of resistive memory devices serves as a non-von Neumann computing core and the results of the computation are not necessarily stored in the memory array. We present applications of this algorithm to process realworld data sets such as weather data

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Conclusion