FeFET and NCFET for Future Neural Networks: Visions and Opportunities
Mikail Yayla1,a, Kuan-Hsun Chen1,b, Georgios Zervakis2,a, Jörg Henkel2,b, Jian-Jia Chen1,c and Hussam Amrouch3
1Department of Computer Science, Technische University Dortmund, Germany
amikail.yayla@tu-dortmund.de
bkuan-hsun.chen@tu-dortmund.de
cjian-jia.chen@cs.uni-dortmund.de
2Department of Computer Science, Karlsruhe Institute of Technology, Germany
ageorgios.zervakis@kit.edu
bhenkel@kit.edu
3Department of Computer Science, University of Stuttgart, Germany
amrouch@iti.uni-stuttgart.de
ABSTRACT
The goal of this special session paper is to introduce and discuss different emerging technologies for logic circuitry and memory as well as new lightweight architectures for neural networks. We demonstrate how the ever-increasing complexity in Artificial Intelligent (AI) applications, resulting in an immense increase in the computational power, necessitates inevitably employing innovations starting from the underlying devices all the way up to the architectures. Two different promising emerging technologies will be presented: (i) Negative Capacitance Field- Effect Transistor (NCFET) as a new beyond-CMOS technology with advantages for offering low power and/or higher accuracy for neural network inference. (ii) Ferroelectric FET (FeFET) as a novel non-volatile, area-efficient and ultra-low power memory device. In addition, we demonstrate how Binarized Neural Networks (BNNs) offer a promising alternative for traditional Deep Neural Networks (DNNs) due to its lightweight hardware implementation. Finally, we present the challenges from combining FeFET-based NVM with NNs and summarize our perspectives for future NNs and the vital role that emerging technologies may play.
