@inproceedings{49f3b4da494c4d41a3ae290f2f77fc02,
title = "Ultra Low Power 3D-Embedded Convolutional Neural Network Cube Based on α-IGZO Nanosheet and Bi-Layer Resistive Memory",
abstract = "In this paper we propose and evaluate the performance of a 3D-embedded neuromorphic computation block based on indium gallium zinc oxide (α-IGZO) based nanosheet transistor and bi-layer resistive memory devices. We have fabricated bi-layer resistive random-access memory (RRAM) devices with Ta2O5 and Al2O3 layers. The device has been characterized and modeled. The compact models of RRAM and α-IGZO based embedded nanosheet structures have been used to evaluate the system level performance of 8 vertically stacked α-IGZO based nanosheet layers with RRAM for neuromorphic applications. The model considers the design space with uniform bit line (BL), select line (SL) and word line (WL) resistance. Finally, we have simulated the weighted sum operation with our proposed 8-layer stacked nanosheet based embedded memory and evaluated the performance for VGG-16 convolutional neural network (CNN) for Fashion-MNIST and CIFAR-10 data recognition, which yielded 92% and 75% accuracy respectively with drop out layers amid device variation. ",
keywords = "3D-IC, CNN, Neuromorphic Computing, RRAM, α-IGZO",
author = "Sunanda Thunder and Parthasarathi Pal and Wang, {Yeong Her} and Po-Tsang Huang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on IC Design and Technology, ICICDT 2021 ; Conference date: 15-09-2021 Through 17-09-2021",
year = "2021",
month = jul,
doi = "10.1109/ICICDT51558.2021.9626489",
language = "English",
series = "2021 International Conference on IC Design and Technology, ICICDT 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2021 International Conference on IC Design and Technology, ICICDT 2021",
address = "United States",
}