@inproceedings{405dac956bad4373936057964b39e214,
title = "Benchmarking the performance of heterogeneous stacked RRAM with CFETSRAM and MRAM for deep neural network application amidst variation and noise",
abstract = "In this article we demonstrate and compare the performance of 32nm technology node compatible high-K and low-K stacked RRAM with CFET-SRAM and MRAM for binary deep neural network. We have fabricated heterogenous stacked RRAM with Sidoped Al2O3 and Ta2O5 as stacked layer for synaptic memory application. The device demonstrated an exorbitant on/off ratio ~ 4.2 x 103 with an ultra-low variation (σ ~ 6E-07 S). We have trained the neural network with 97.11% accuracy as baseline and observed the impact of conductance variation and read noise variation. We have also benchmarked the performance of our device with CFET-SRAM and MRAM technologies from other works and observed superior performance of our devices in terms of accuracy. ",
keywords = "Bipolar resistive switching, Neural network, Recognition accuracy, ReRAM, Synaptic simulation",
author = "Parthasarathi Pal and Sunanda Thunder and Tsai, {Min Jung} and Po-Tsang Huang and Wang, {Yeong Her}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021 ; Conference date: 19-04-2021 Through 22-04-2021",
year = "2021",
month = apr,
day = "19",
doi = "10.1109/VLSI-TSA51926.2021.9440130",
language = "English",
series = "VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--2",
booktitle = "VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings",
address = "United States",
}