5 papers
Reservoir Computing with Heterogeneous Magnetic Metamaterials
R. Yagan, C. Swindells, I. T. Vidamour +5
Physical reservoir computing utilizes the intrinsic nonlinear and history-dependent dynamics of physical systems to perform machine-learning tasks with minimal training overhead. H…
Reproducible Reservoir Computing with Thermally Driven Superparamagnets: Controlling Temperature Sensitivity
Zhengfei Chen, Alex Welbourne, Matthew O. A. Ellis +3
Unconventional computing systems must demonstrate robust performance under real-world environmental conditions to enable practical deployments. We have recently proposed superparam…
Low-power analogue neural networks with trainable nonlinear connections for continuous control
Ian T. Vidamour, Fernando Aguirre, Thomas J. Hayward +13
Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as sc…
Learning Nonlinear Heterogeneity in Physical Kolmogorov-Arnold Networks
Fabiana Taglietti, Andrea Pulici, Maxwell Roxburgh +10
Physical neural networks typically train linear synaptic weights while treating device nonlinearities as fixed. We show the opposite - by training the synaptic nonlinearity itself,…
Metrics for spin-based computing
Hidekazu Kurebayashi, Giovanni Finocchio, Karin Everschor-Sitte +10
Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by elect…