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Gradient-based optimization of spintronic devices

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Yusuke Imai, Shuhong Liu, Nozomi Akashi, Kohei Nakajima

Mesoscale and Nanoscale Physics · Submitted 13 Sep 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This approach allowed us to estimate how to control the experimental setup and design the physical systems so that the task could be solved with a high accuracy using spin-torque oscillators.

how it is stated · asserted 0.85 read the record →

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arXiv ID2409.09105 (version 1)
Pith IDOZN4YZQV, Pith's permanent record of this paper and its review
CitationKohei Nakajima, Nozomi Akashi, Shuhong Liu, Yusuke Imai. "Gradient-based optimization of spintronic devices." arXiv:2409.09105 (2024). https://pith.science/paper/OZN4YZQV
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,591 input tokens.

What the abstract itself states, as probabilities: claims first 0.09 · states limitation 0.17 · human participants 0.02 · controlled comparison 0.12 · randomized assignment 0.01 · states quantitative result 0.08 · compares with prior results 0.46 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The optimization of physical parameters serves various purposes, such as system identification and efficiency in developing devices. Spin-torque oscillators have been applied to neuromorphic computing experimentally and theoretically, but the optimization of their physical parameters has usually been done by grid search. In this paper, we propose a scheme to optimize the parameters of the dynamics of macrospin-type spin-torque oscillators using the gradient descent method with automatic differentiation. First, we prepared numerically created dynamics as teacher data and successfully tuned the parameters to reproduce the dynamics. This can be applied to obtain the correspondence between the simulation and experiment of the spin-torque oscillators. Next, we successfully solved the image recognition task with high accuracy by connecting the coupled system of spin-torque oscillators to the input and output layers and training all of them through gradient descent. This approach allowed us to estimate how to control the experimental setup and design the physical systems so that the task could be solved with a high accuracy using spin-torque oscillators.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The optimization of physical parameters serves various purposes, such as system identification and efficiency in developing devices. background 1.00 0.05 0.03 asserted 0.98
s1 Spin-torque oscillators have been applied to neuromorphic computing experimentally and theoretically, but the optimization of their physical parameters has usually been done by grid search. background 0.61 0.05 0.03 asserted 1.00
s2 In this paper, we propose a scheme to optimize the parameters of the dynamics of macrospin-type spin-torque oscillators using the gradient descent method with automatic differentiation. contribution 0.94 0.78 0.14 asserted 0.97
s3 First, we prepared numerically created dynamics as teacher data and successfully tuned the parameters to reproduce the dynamics. result 0.50 0.95 0.05 asserted 1.00
s4 This can be applied to obtain the correspondence between the simulation and experiment of the spin-torque oscillators. interpretation 0.88 0.26 0.06 hedged possibility 0.87
s5 Next, we successfully solved the image recognition task with high accuracy by connecting the coupled system of spin-torque oscillators to the input and output layers and training all of them through gradient descent. result 0.93 0.95 0.29 asserted 1.00
s6 This approach allowed us to estimate how to control the experimental setup and design the physical systems so that the task could be solved with a high accuracy using spin-torque oscillators. interpretation 0.98 0.86 0.45 asserted 0.85

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The optimization of physical parameters serves various purposes, such as system identification and efficiency in developing devices. Spin-torque oscillators have been applied to neuromorphic computing experimentally and theoretically, but the optimization of their physical parameters has usually been done by grid search. In this paper, we propose a scheme to optimize the parameters of the dynamics of macrospin-type spin-torque oscillators using the gradient descent method with automatic differentiation. First, we prepared numerically created dynamics as teacher data and successfully tuned the parameters to reproduce the dynamics. This can be applied to obtain the correspondence between the simulation and experiment of the spin-torque oscillators. Next, we successfully solved the image recognition task with high accuracy by connecting the coupled system of spin-torque oscillators to the input and output layers and training all of them through gradient descent. This approach allowed us to estimate how to control the experimental setup and design the physical systems so that the task could be solved with a high accuracy using spin-torque oscillators.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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