Computing with injection-locked spintronic diodes

TL;DR

This paper explores the use of spintronic diodes for neuromorphic computing, demonstrating their ability to perform analog multiplication crucial for CNNs, through the concept of degree of rectification and micromagnetic simulations.

Contribution

It introduces the concept of degree of rectification in injection-locked spintronic diodes and demonstrates their application in neuromorphic computing and image recognition.

Findings

Successful design of STDs for DOR implementation

Validation through micromagnetic simulations and experimental data

Effective use in image processing and digit recognition

Abstract

Spintronic diodes (STDs) are emerging as a technology for the realization of high-performance microwave detectors. The key advantages of such devices are their high sensitivity, capability to work at low input power, and compactness. In this work, we show a possible use of STDs for neuromorphic computing expanding the realm of their functionalities to implement analog multiplication, which is a key operation in convolutional neural networks (CNN). In particular, we introduce the concept of degree of rectification (DOR) in injection-locked STDs. Micromagnetic simulations are used to design and identify the working range of the STDs for the implementation of the DOR. Previous experimental data confirm the applicability of the proposed solution, which is tested in image processing and in a CNN that recognizes handwritten digits.