Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays

Changpeng Ti, Usman Hassan, Sairam Sri Vatsavai, Margaret McCarter, Aastha Vasdev, Jincheng An, Barat Achinuq, Ulrich Welp, Sen-Ching Cheung, Ishan G. Thakkar, J. Todd Hastings

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and high-dimensional nonlinearity. These PNA reservoir systems from prior works leverage echo state properties and linear/nonlinear short-term memory of component reservoir nodes to map and preserve the dynamical information of the input time-series data into nondelay spatial embeddings. Such mappings enable these PNA reservoir systems to imitate and predict/forecast the input time series data. However, these prior PNA reservoir systems are based solely on the nondelay spatial embeddings obtained at component reservoir nodes. As a result, they require a massive number of component reservoir nodes, or a very large spatial embedding (i.e., high-dimensional spatial embedding) per reservoir node, or both, to achieve acceptable imitation and prediction accuracy. These requirements reduce the practical feasibility of such PNA reservoir systems. To address this shortcoming, we present a mixed delay/nondelay embeddings-based PNA reservoir system. Our system uses a single PNA reservoir node with the ability to obtain a mixture of delay/nondelay embeddings of the dynamical information of the time-series data applied at the input of a single PNA reservoir node. Our analysis shows that when these mixed delay/nondelay embeddings are used to train a perceptron at the output layer, our reservoir system outperforms existing PNA-based reservoir systems for the imitation of NARMA 2, NARMA 5, NARMA 7, and NARMA 10 time series data, and for the short-term and long-term prediction of the Mackey Glass time series data.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Rebooting Computing, ICRC 2024
ISBN (Electronic)9798331541279
DOIs
StatePublished - 2024
Event9th Annual IEEE International Conference on Rebooting Computing, ICRC 2024 - San Diego, United States
Duration: Dec 16 2024Dec 17 2024

Publication series

Name2024 IEEE International Conference on Rebooting Computing, ICRC 2024

Conference

Conference9th Annual IEEE International Conference on Rebooting Computing, ICRC 2024
Country/TerritoryUnited States
CitySan Diego
Period12/16/2412/17/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC-0024346. This research used resources of the Advanced Light Source, a U.S. DOE Office of Science User Facility under contract no. DEAC02-05CH11231.

FundersFunder number
U.S. Department of Energy Oak Ridge National Laboratory U.S. Department of Energy National Science Foundation National Energy Research Scientific Computing Center
DOE Basic Energy SciencesDE-SC-0024346
DOE Basic Energy Sciences
National Science Foundation Office of International Science and EngineeringDEAC02-05CH11231
National Science Foundation Office of International Science and Engineering

    Keywords

    • Delay-Nondelay Embeddings
    • Geometrical Frustration
    • Nanomagnet Arrays
    • Reservoir

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Hardware and Architecture
    • Safety, Risk, Reliability and Quality

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