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

• 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: Article
• Language: English
• Published: 2021
• Subjects: Science::Physics; Convolutional Neural Network; Look-up-table
• Online Access: https://hdl.handle.net/10356/148658
• Institution: Nanyang Technological University

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...