TY - GEN
T1 - Modeling of variability-aware memristive neural networks
AU - Sasikumar, Renjith
AU - Ganapathi, K. Lakshmi
AU - Misra, Durgamadhab
AU - Padmanabhan, Revathy
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85167873101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85167873101&partnerID=8YFLogxK
U2 - 10.1109/DRC58590.2023.10187082
DO - 10.1109/DRC58590.2023.10187082
M3 - Conference contribution
AN - SCOPUS:85167873101
T3 - Device Research Conference - Conference Digest, DRC
BT - 2023 Device Research Conference, DRC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Device Research Conference, DRC 2023
Y2 - 25 June 2023 through 28 June 2023
ER -