Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks.

ABSTRACT: Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever-increasing computing requirements of these structures have contributed to a desire for novel technologies and paradigms, including memristor-based hardware accelerators. Solutions based on memristive crossbars and analog data processing promise to improve the overall energy efficiency. However, memristor nonidealities can lead to the degradation of neural network accuracy, while the attempts to mitigate these negative effects often introduce design trade-offs, such as those between power and reliability. In this work, authors design nonideality-aware training of memristor-based neural networks capable of dealing with the most common device nonidealities. The feasibility

SUBMITTER: Joksas D 

PROVIDER: S-EPMC9189678 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature