An energy efficient neuron is essential for spiking neural network (SNN) to operate at low energy to mimic the human brain functionalities in hardware. Several CMOS-based Si transistors, memory devices, spintronic devices have been used as a neuron for SNN. However, the main concern is the energy efficiency for these neurons. In this letter, we experimentally demonstrate a Si-based CMOS compatible asymmetric NIPIN diode as a LIF neuron. First, we demonstrate the LIF neuron characteristics by comparing the spike-frequency (f) versus voltage curve with that of a simple LIF neuron model. This neuron shows a classical ReLU behavior, which is attractive for typical software neuron models. Then, we show an ultra-low energy consumption of<inline-formula><tex-math notation="LaTeX">$210^-17 J$</tex-math></inline-formula>per spike at 10-nm node of this neuron, as NIPIN diode is highly scalable (<inline-formula><tex-math notation="LaTeX">$4F^2$</tex-math></inline-formula>) due to its capacitorless structure. This is the lowest reported energy/spike for any LIF neuron for SNN application. Thus, the NIPIN is suitable for ultra-low energy LIF neuron application for energy efficient SNN.
%0 Journal Article
%1 das2018ultralow
%A Das, B.
%A Schulze, J.
%A Ganguly, U.
%D 2018
%J IEEE Electron Device Letters
%K iht j.schulze.iht journal
%N 12
%P 1832-1835
%R 10.1109/LED.2018.2876684
%T Ultra-Low Energy LIF Neuron Using Si NIPIN Diode for Spiking Neural Networks
%U https://ieeexplore.ieee.org/document/8496854/
%V 39
%X An energy efficient neuron is essential for spiking neural network (SNN) to operate at low energy to mimic the human brain functionalities in hardware. Several CMOS-based Si transistors, memory devices, spintronic devices have been used as a neuron for SNN. However, the main concern is the energy efficiency for these neurons. In this letter, we experimentally demonstrate a Si-based CMOS compatible asymmetric NIPIN diode as a LIF neuron. First, we demonstrate the LIF neuron characteristics by comparing the spike-frequency (f) versus voltage curve with that of a simple LIF neuron model. This neuron shows a classical ReLU behavior, which is attractive for typical software neuron models. Then, we show an ultra-low energy consumption of<inline-formula><tex-math notation="LaTeX">$210^-17 J$</tex-math></inline-formula>per spike at 10-nm node of this neuron, as NIPIN diode is highly scalable (<inline-formula><tex-math notation="LaTeX">$4F^2$</tex-math></inline-formula>) due to its capacitorless structure. This is the lowest reported energy/spike for any LIF neuron for SNN application. Thus, the NIPIN is suitable for ultra-low energy LIF neuron application for energy efficient SNN.
@article{das2018ultralow,
abstract = {An energy efficient neuron is essential for spiking neural network (SNN) to operate at low energy to mimic the human brain functionalities in hardware. Several CMOS-based Si transistors, memory devices, spintronic devices have been used as a neuron for SNN. However, the main concern is the energy efficiency for these neurons. In this letter, we experimentally demonstrate a Si-based CMOS compatible asymmetric NIPIN diode as a LIF neuron. First, we demonstrate the LIF neuron characteristics by comparing the spike-frequency (f) versus voltage curve with that of a simple LIF neuron model. This neuron shows a classical ReLU behavior, which is attractive for typical software neuron models. Then, we show an ultra-low energy consumption of<inline-formula><tex-math notation="LaTeX">$\sim \text {2}\times \text {10}^{-\text {17}} {J}$</tex-math></inline-formula>per spike at 10-nm node of this neuron, as NIPIN diode is highly scalable (<inline-formula><tex-math notation="LaTeX">$\text {4}{F}^{\text {2}}$</tex-math></inline-formula>) due to its capacitorless structure. This is the lowest reported energy/spike for any LIF neuron for SNN application. Thus, the NIPIN is suitable for ultra-low energy LIF neuron application for energy efficient SNN.},
added-at = {2018-11-26T12:49:11.000+0100},
author = {Das, B. and Schulze, J. and Ganguly, U.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2e54c71a945f999824a6cc89ea4323739/ihtpublikation},
doi = {10.1109/LED.2018.2876684},
interhash = {41b0991f14e4321eefe39d69a2fe566c},
intrahash = {e54c71a945f999824a6cc89ea4323739},
issn = {0741-3106},
journal = {IEEE Electron Device Letters},
keywords = {iht j.schulze.iht journal},
month = dec,
number = 12,
pages = {1832-1835},
timestamp = {2018-11-26T11:49:11.000+0100},
title = {Ultra-Low Energy LIF Neuron Using Si NIPIN Diode for Spiking Neural Networks},
url = {https://ieeexplore.ieee.org/document/8496854/},
volume = 39,
year = 2018
}