Publication

Neuromorphic Computing Using High Magnetoresistance Ratios in Co <sub>2</sub> MnSi/MgO/Co <sub>2</sub> MnSi Magnetic Tunnel Junctions

2026

Abstract

Neuromorphic computing aims to achieve energy-efficient parallel processing by emulating the human brain. Computation-in-memory (CiM) using magnetic tunnel junction (MTJ) is a promising approach due to its non-volatility, high endurance, and CMOS compatibility. Co 2 MnSi/MgO/Co 2 MnSi MTJs show high tunneling magnetoresistance (TMR) ratios. We design and analyze binary neural networks (BNNs) on MTJ-CiM circuits by comparing time- and voltage-difference methods using circuit simulations. Design guidelines are derived from resistance scaling, delay, and TMR-dependent margins. To counter binarization accuracy loss of the BNN, we apply gradient descent with binary weights with a scheduling scheme and achieve 94% accuracy on the MNIST dataset. Using higher TMR improves robustness against MTJ variability and limits reductions in accuracy to

Official source

https://infoscience.epfl.ch/entities/publication/1afa83fa-112e-4ab5-8e2f-d24a88139efa

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2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/1afa83fa-112e-4ab5-8e2f-d24a88139efa

About this result

Related concepts (32)

Neuromorphic engineering

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration).

Quantum computing

A quantum computer is a computer that exploits quantum mechanical phenomena. At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer.

Computational neuroscience

Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous.

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Neuromorphic Computing Using High Magnetoresistance Ratios in Co <sub>2</sub> MnSi/MgO/Co <sub>2</sub> MnSi Magnetic Tunnel Junctions

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