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Hardware-compatible and energy-efficient neural network for solving machine learning tasks

David, Aurian
(2024)

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Details

Supervisors
Abreu Araujo, Flavio
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] : ingénieur civil électricien, à finalité spécialisée
Abstract
This study explores the fusion of machine learning, particularly reservoir computing networks (RCNs), with spintronics to achieve energy-efficient hardware implementations. The focus is on utilizing Spin Torque Vortex Oscillators (STVOs) as “neurons” within a reservoir computing network. By leveraging the unique properties of STVOs, the research aims to assess their performance in comparison to other hardware-implemented and conventional reservoir computing networks. The investigation includes a comprehensive analysis of STVO-specific parameters and their impact on the overall system’s efficiency and computational capability. This work contributes to the advancement of energy-efficient machine learning hardware, demonstrating the potential of STVOs in enhancing the performance of reservoir computing networks.

Citations

  • APA

David, A. (2024). Hardware-compatible and energy-efficient neural network for solving machine learning tasks. https://dial-mem.test.bib.ucl.ac.be/handle/2078.2/39734