Supervised Learning in All FeFET-Based Spiking Neural Network: Opportunities and Challenges

Frontiers in Neuroscience
Sourav DuttaSuman Datta

Abstract

The two possible pathways toward artificial intelligence (AI)-(i) neuroscience-oriented neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science driven machine learning (like deep learning) differ widely in their fundamental formalism and coding schemes (Pei et al., 2019). Deviating from traditional deep learning approach of relying on neuronal models with static nonlinearities, SNNs attempt to capture brain-like features like computation using spikes. This holds the promise of improving the energy efficiency of the computing platforms. In order to achieve a much higher areal and energy efficiency compared to today's hardware implementation of SNN, we need to go beyond the traditional route of relying on CMOS-based digital or mixed-signal neuronal circuits and segregation of computation and memory under the von Neumann architecture. Recently, ferroelectric field-effect transistors (FeFETs) are being explored as a promising alternative for building neuromorphic hardware by utilizing their non-volatile nature and rich polarization switching dynamics. In this work, we propose an all FeFET-based SNN hardware that allows low-power spike-based information processing and co-localized memory and computing (a...Continue Reading

References

Jun 27, 2000·Biophysical Journal·L J GentetJ D Clements
Apr 24, 2001·Journal of Computational Neuroscience·Y H Liu, X J Wang
Oct 28, 2003·Neural Computation·Jan Benda, Andreas V M Herz
Mar 11, 2006·IEEE Transactions on Neural Networks·Giacomo IndiveriRodney Douglas
Feb 5, 2008·IEEE Transactions on Neural Networks·E M Izhikevich
Mar 6, 2008·Nature Reviews. Neuroscience·A Aldo FaisalDaniel M Wolpert
Jul 13, 2011·Frontiers in Neuroscience·Giacomo IndiveriKwabena Boahen
Sep 21, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·José Antonio Pérez-CarrascoBernabé Linares-Barranco
Oct 12, 2013·Frontiers in Neuroscience·Peter O'ConnorMichael Pfeiffer
Jan 29, 2014·Current Opinion in Neurobiology·Robert Gütig
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Jan 1, 2015·Frontiers in Computational Neuroscience·Peter U Diehl, Matthew Cook
May 18, 2016·Nature Nanotechnology·Tomas TumaEvangelos Eleftheriou
Jul 23, 2016·Scientific Reports·Abhronil SenguptaKaushik Roy
Jan 13, 2018·Neural Networks : the Official Journal of the International Neural Network Society·Saeed Reza KheradpishehTimothée Masquelier
Apr 14, 2018·Neural Computation·Friedemann Zenke, Surya Ganguli
Apr 26, 2018·Frontiers in Neuroscience·Runchun M WangAndré van Schaik
Jun 8, 2018·Nature·Stefano AmbrogioGeoffrey W Burr
Jun 8, 2018·Frontiers in Neuroscience·Yujie WuLuping Shi
Jun 28, 2018·ACS Applied Materials & Interfaces·Halid MulaosmanovicStefan Slesazeck
Oct 31, 2018·Frontiers in Neuroscience·Amirreza YousefzadehBernabé Linares-Barranco
Nov 16, 2018·Nanoscale·Halid MulaosmanovicStefan Slesazeck
Mar 23, 2019·Frontiers in Neuroscience·Abhronil SenguptaKaushik Roy
Oct 9, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Nassim AbderrahmaneBenoît Miramond

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Citations

Aug 24, 2021·Frontiers in Neuroscience·Zong-Xiao LiFei Zhuge
Jul 29, 2021·Nanotechnology·Halid MulaosmanovicStefan Slesazeck
Nov 2, 2021·ACS Applied Materials & Interfaces·Duho KimChanghwan Choi

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Methods Mentioned

BETA
chip

Software Mentioned

SNN
SPICE
TensorFlow
PyTorch

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