@inproceedings{59fc35af9d224583a2ae74268b910128,
title = "Biologically Plausible Ferroelectric Quasi-Leaky Integrate and Fire Neuron",
abstract = "Biologically plausible mechanism like homeostasis compliments Hebbian learning to allow unsupervised learning in spiking neural networks [1]. In this work, we propose a novel ferroelectric-based quasi-LIF neuron that induces intrinsic homeostasis. We experimentally characterize and perform phase-field simulations to delineate the non-trivial transient polarization relaxation mechanism associated with multi-domain interaction in poly-crystalline ferroelectric, such as Zr doped HfO2, that underlines the Q-LIF behavior. Network level simulations with the Q-LIF neuron model exhibits a 2.3x reduction in firing rate compared to traditional LIF neuron while maintaining iso-accuracy of 84-85\% across varying network sizes. Such an energy-efficient hardware for spiking neuron can enable ultra-low power data processing in energy constrained environments suitable for edge-intelligence.",
author = "S. Dutta and A. Saha and P. Panda and W. Chakraborty and J. Gomez and A. Khanna and S. Gupta and K. Roy and S. Datta",
note = "Publisher Copyright: {\textcopyright} 2019 The Japan Society of Applied Physics.; 39th Symposium on VLSI Technology, VLSI Technology 2019 ; Conference date: 09-06-2019 Through 14-06-2019",
year = "2019",
month = jun,
doi = "10.23919/VLSIT.2019.8776487",
language = "English (US)",
series = "Digest of Technical Papers - Symposium on VLSI Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "T140--T141",
booktitle = "2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers",
address = "United States",
}