Abstract
In the resistive random access memory (RRAM) crossbar array, the sneak path problem severely degrades the reliability of data stored in the memory cell. In this paper, we apply the deep learning technique and propose a deep neural network (DNN) architecture together with a novel data preprocessing scheme for signal detection of RRAM in the presence of the sneak path interference. Simulation results demonstrate that our proposed DNN based detector with data preprocessing can effectively mitigate the sneak path effect without prior knowledge of the channel.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
