# Domain wall motion-driven magnetic convolutional accelerator

## TL;DR

This paper introduces a new magnetic computing platform that uses domain wall motion to perform efficient convolution operations for AI and signal processing.

## Contribution

A novel compute-in-memory architecture using magnetic domain wall motion for energy-efficient convolution operations.

## Key findings

- The platform achieves 10^3 to 10^5 improvements in area, energy, and throughput over existing technologies.
- The system supports Fourier analysis, neural networks, and image processing with nonvolatile memory.
- Domain wall motion enables computation that unifies memory and processing.

## Abstract

Modern computing powers applications from data analysis to artificial intelligence but now faces limitations. The slowdown of device scaling and the bottleneck between memory and processors motivate architectures that unify computation and data storage. Convolution is a core operation in learning, vision, and signal processing, yet its conventional implementation incurs high energy, high latency, and limited scalability. Magnetic systems that host spin textures, such as domain walls, offer dynamic behaviors that enable computation beyond traditional logic. Here we introduce a compute-in-memory platform that performs convolution by sequentially shifting magnetic domains and sensing the resulting signals. Information is written directly into domain patterns, processed through controlled motion, and read electrically, forming a nonvolatile structure suited for convolution tasks. This approach supports applications including Fourier analysis, neural networks, and image processing, achieving 103 to 105 improvements in area, energy, and throughput over existing technologies, marking a concrete advance in spintronic computing.

The authors demonstrate a magnetic compute-in-memory platform that performs convolution using domain wall motion, enabling highly energy- and area-efficient processing for future AI hardware.