Modeling of variability-aware memristive neural networks

Renjith Sasikumar, K. Lakshmi Ganapathi, Durgamadhab Misra, Revathy Padmanabhan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In recent years, memristive neuromorphic systems have gained much attention. In this work, we developed a physics-based framework to model transport in valence change memory (VCM) memristors, implemented in Verilog-A. This has enabled us to scale up and simulate the performance of these devices in a crossbar array/neural network for pattern classification, for instance. The system's performance is analyzed based on classification accuracy in different conditions. We anticipate that this will provide useful insights into the design of these systems by analyzing their performance, based on our model.

Original languageEnglish (US)
Title of host publication2023 Device Research Conference, DRC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350323108
StatePublished - 2023
Event2023 Device Research Conference, DRC 2023 - Santa Barbara, United States
Duration: Jun 25 2023Jun 28 2023

Publication series

NameDevice Research Conference - Conference Digest, DRC
ISSN (Print)1548-3770


Conference2023 Device Research Conference, DRC 2023
Country/TerritoryUnited States
CitySanta Barbara

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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