Exploring the impact of variability in resistance distributions of RRAM on the prediction accuracy of deep learning neural networks

Exploring the impact of variability in resistance distributions of RRAM on the prediction accuracy of deep learning neural networks

In this work, we explore the use of the resistive random access memory (RRAM) device as a synapse for mimicking the trained weights linking neurons in a deep learning neural network (DNN) (AlexNet). The RRAM devices were fabricated in-house and subjected to 1000 bipolar read-write cycles to measure...

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Bibliographic Details
Main Authors: Prabhu, Nagaraj Lakshmana, Loy, Desmond Jia Jun, Dananjaya, Putu Andhita, Lew, Wen Siang, Toh, Eng Huat, Raghavan, Nagarajan
Other Authors: School of Physical and Mathematical Sciences
Format: Journal Article
Language:English
Published: 2021
Subjects:
Science::Physics
Convolutional Neural Network
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Online Access:https://hdl.handle.net/10356/148658

Internet

https://hdl.handle.net/10356/148658

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