Neural-like computing with populations of superparamagnetic basis functions

TL;DR

This paper demonstrates that nanoscale magnetic tunnel junctions can form basis functions for population coding, enabling fault-tolerant, low-power computing with noisy nanodevices, and shows their application in learning non-linear transformations.

Contribution

It provides the first experimental demonstration of population coding with nanodevices, specifically magnetic tunnel junctions, and designs hybrid systems for resilient computation.

Findings

Nanoscale magnetic tunnel junctions can implement basis functions.

A population of nine junctions can generate cursive letters.

Hybrid magnetic-CMOS systems can learn non-linear transformations.

Abstract

In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for calculating. For this purpose, the responses of the nanodevices should be non-linear, and each tuned to different values of the input. These strong requirements have prevented a demonstration of population coding with nanodevices. Here, we show that nanoscale magnetic tunnel junctions can be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variability-resilient transformations with a low imprint area and low power.