Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

Abstract Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor...

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Autores principales:	Finn Zahari, Eduardo Pérez, Mamathamba Kalishettyhalli Mahadevaiah, Hermann Kohlstedt, Christian Wenger, Martin Ziegler
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1a4c1d13dc044dc49d23d1a716766d64
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spelling	oai:doaj.org-article:1a4c1d13dc044dc49d23d1a716766d642021-12-02T19:09:30ZAnalogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices10.1038/s41598-020-71334-x2045-2322https://doaj.org/article/1a4c1d13dc044dc49d23d1a716766d642020-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-71334-xhttps://doaj.org/toc/2045-2322Abstract Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.Finn ZahariEduardo PérezMamathamba Kalishettyhalli MahadevaiahHermann KohlstedtChristian WengerMartin ZieglerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Finn Zahari Eduardo Pérez Mamathamba Kalishettyhalli Mahadevaiah Hermann Kohlstedt Christian Wenger Martin Ziegler Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
description	Abstract Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.
format	article
author	Finn Zahari Eduardo Pérez Mamathamba Kalishettyhalli Mahadevaiah Hermann Kohlstedt Christian Wenger Martin Ziegler
author_facet	Finn Zahari Eduardo Pérez Mamathamba Kalishettyhalli Mahadevaiah Hermann Kohlstedt Christian Wenger Martin Ziegler
author_sort	Finn Zahari
title	Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
title_short	Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
title_full	Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
title_fullStr	Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
title_full_unstemmed	Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
title_sort	analogue pattern recognition with stochastic switching binary cmos-integrated memristive devices
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1a4c1d13dc044dc49d23d1a716766d64
work_keys_str_mv	AT finnzahari analoguepatternrecognitionwithstochasticswitchingbinarycmosintegratedmemristivedevices AT eduardoperez analoguepatternrecognitionwithstochasticswitchingbinarycmosintegratedmemristivedevices AT mamathambakalishettyhallimahadevaiah analoguepatternrecognitionwithstochasticswitchingbinarycmosintegratedmemristivedevices AT hermannkohlstedt analoguepatternrecognitionwithstochasticswitchingbinarycmosintegratedmemristivedevices AT christianwenger analoguepatternrecognitionwithstochasticswitchingbinarycmosintegratedmemristivedevices AT martinziegler analoguepatternrecognitionwithstochasticswitchingbinarycmosintegratedmemristivedevices
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Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

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