Skip to main content
King Abdullah University of Science and Technology
Cell Visualization Summit
Cellvis
Cell Visualization Summit

Main navigation

  • Home
  • People
    • All Profiles
    • Organizers
    • Keynote Speakers
    • Attendees
  • Events
    • All Events
    • Events Calendar

analogue integrated circuits

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

1 min read · Tue, Jan 1 2019

News

analogue integrated circuits CMOS integrated circuits Circuits

Olga Krestinskaya, et al., "Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits" IEEE Transactions on Circuits and Systems-I 66 (2), 2019, 719. The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we propose analog backpropagation learning circuits for various memristive learning architectures, such as deep neural

Cell Visualization Summit (Cellvis)

Footer

  • A-Z Directory
    • All Content
    • Browse Related Sites
  • Site Management
    • Log in

© 2025 King Abdullah University of Science and Technology. All rights reserved. Privacy Notice