A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption.

ABSTRACT: Physical neural networks (PNN) using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training PNN is difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e., training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via an autonomous physical process, eliminating the requirements on external computation resources. Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN), and unsupervised learning is intrinsically performed via the e

SUBMITTER: Niu C 

PROVIDER: S-EPMC11665918 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature