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

• المؤلفون الرئيسيون: Prabhu, Nagaraj Lakshmana, Loy, Desmond Jia Jun, Dananjaya, Putu Andhita, Lew, Wen Siang, Toh, Eng Huat, Raghavan, Nagarajan
• مؤلفون آخرون: School of Physical and Mathematical Sciences
• التنسيق: مقال
• اللغة: English
• منشور في: 2021
• الموضوعات: Science::Physics; Convolutional Neural Network; Look-up-table
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/148658

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...