# Analog Signal Processing Using Stochastic Magnets

## TL;DR

This paper introduces a novel low barrier magnet hardware unit that functions as an analog stochastic neuron, enabling real-time temporal sequence learning and prediction for neuromorphic computing applications.

## Contribution

It presents a compact, scalable hardware implementation of leaky-integrate-and-fire neurons using magnetic tunnel junctions coupled with CMOS buffers, advancing neuromorphic hardware design.

## Key findings

- Demonstrated real-time temporal sequence learning and prediction.
- Showed scalable and adaptive signal processing capabilities.
- Enabled integration of hardware-based cognition in various systems.

## Abstract

We present a low barrier magnet based compact hardware unit for analog stochastic neurons and demonstrate its use as a building-block for neuromorphic hardware. By coupling circular magnetic tunnel junctions (MTJs) with a CMOS based analog buffer, we show that these units can act as leaky-integrate-and fire (LIF) neurons, a model of biological neural networks particularly suited for temporal inferencing and pattern recognition. We demonstrate examples of temporal sequence learning, processing, and prediction tasks in real time, as a proof of concept demonstration of scalable and adaptive signal-processors. Efficient non von-Neumann hardware implementation of such processors can open up a pathway for integration of hardware based cognition in a wide variety of emerging systems such as IoT, industrial controls, bio- and photo-sensors, and Unmanned Autonomous Vehicles.