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In-Memory Mathematical Operations with Spin-Orbit Torque Devices.

Ruofan Li1, Min Song2, Zhe Guo1

  • 1School of Optical and Electronic Information & Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.

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Summary
This summary is machine-generated.

This study demonstrates in-memory analog arithmetic operations using spin-orbit torque (SOT) devices for efficient artificial intelligence (AI) and signal processing. These compact SOT devices enable high-accuracy pattern recognition and image processing tasks.

Keywords:
analog mathematical computingimage and signal processingin-memory computingneural networkspin-orbit torque

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Area of Science:

  • * Physics and Materials Science
  • * Computer Engineering
  • * Artificial Intelligence

Background:

  • * Analog arithmetic operations are crucial for image/signal processing and artificial intelligence (AI).
  • * In-memory computing (IMC) promises high performance and energy efficiency.
  • * Current IMC implementations use discrete components, hindering scalability and integration.

Purpose of the Study:

  • * To experimentally demonstrate prototypical in-memory analog arithmetic operations (summation, subtraction, multiplication).
  • * To leverage spin-orbit torque (SOT) devices for compact and integrated analog computing.
  • * To showcase applications in image edge detection, signal modulation, and artificial neural networks (ANNs).

Main Methods:

  • * Utilized in-memory electrical current sensing units based on spin-orbit torque (SOT) devices.
  • * Developed novel structures for analog arithmetic operations.
  • * Integrated SOT devices into an artificial neural network (ANN) for pattern recognition.

Main Results:

  • * Achieved significantly smaller device footprints compared to state-of-the-art CMOS counterparts.
  • * Successfully implemented analog arithmetic operations, image edge detection, and signal amplitude modulation.
  • * Demonstrated high accuracy (≈95%) in pattern recognition using an SOT-based ANN.

Conclusions:

  • * SOT devices offer a scalable and efficient platform for in-memory analog computing.
  • * The proposed approach enables local data storage and computation, reducing energy consumption.
  • * This technology holds significant potential for advancing AI hardware and signal processing applications.