I. Mulet, B. Theilman, K. P. Patel, J. Arzate, A. Maicke, J. D. Smith, J. B. Aimone, S. G. Cardwell, J. A. C. Incorvia and C. D. Schuman

December, 2024

IEEE International Conference on Rebooting Computing (ICRC)

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Abstract

Novel algorithms leveraging neuromorphic computation are on the forefront of algorithm design. Here, we investigate how stochastic devices integrate and perform with a novel neuromorphic algorithm for solving MAXCUT problems in graphs. We evaluate how using magnetic tunneling junctions (MTJs) as the device to generate random numbers impacts the neuromorphic MAXCUT algorithm. We use both experimental MTJ data, as well as a model of the device behavior to investigate MTJ performance on this task. We also leverage the use of evolutionary optimization to tune the MTJ device to maximize performance on the algorithm and minimize energy usage of the device.

Citation Information

Text

author     I. Mulet and B. Theilman and K. P. Patel and J. Arzate and A. Maicke and
           J. D. Smith and J. B. Aimone and S. G. Cardwell and J. A. C. Incorvia and C. D. Schuman
title      Optimization of Magnetic Tunneling Junction Devices for Neuromorphic Circuits for Solving {MAXCUT}
booktitle  IEEE International Conference on Rebooting Computing (ICRC)
month      December
year       2024
address    San Diego
doi        10.1109/ICRC64395.2024.10937020
url        https://ieeexplore.ieee.org/document/10937020

Bibtex

@INPROCEEDINGS{mtp:24:omt,
    author = "I. Mulet and B. Theilman and K. P. Patel and J. Arzate and A. Maicke and
               J. D. Smith and J. B. Aimone and S. G. Cardwell and J. A. C. Incorvia and C. D. Schuman",
    title = "Optimization of Magnetic Tunneling Junction Devices for Neuromorphic Circuits for Solving {MAXCUT}",
    booktitle = "IEEE International Conference on Rebooting Computing (ICRC)",
    month = "December",
    year = "2024",
    address = "San Diego",
    doi = "10.1109/ICRC64395.2024.10937020",
    url = "https://ieeexplore.ieee.org/document/10937020"
}