Memristor-based feature learning for pattern classification.

ABSTRACT: Inspired by biological processes, feature learning techniques, such as deep learning, have achieved great success in various fields. However, since biological organs may operate differently from semiconductor devices, deep models usually require dedicated hardware and are computation-complex. High energy consumption has made deep model growth unsustainable. We present an approach that directly implements feature learning using semiconductor physics to minimize disparity between model and hardware. Following this approach, a feature learning technique based on memristor drift-diffusion kinetics is proposed by leveraging the dynamic response of a single memristor to learn features. The model parameters and computational operations of the kinetics-based network are reduced by up to 2 and 4 or

SUBMITTER: Shi T 

PROVIDER: S-EPMC11751336 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature