Logic Computing with Stateful Neural Networks of Resistive Switches

Brain‐inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial intelligent tasks. By adopting binary inputs/outputs, neural networks can be used to perform Boolean logic operations, thus potentially surpassing...

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Vydané v:	Advanced materials (Weinheim) Ročník 30; číslo 38; s. e1802554 - n/a
Hlavní autori:	Sun, Zhong, Ambrosi, Elia, Bricalli, Alessandro, Ielmini, Daniele
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Germany Wiley Subscription Services, Inc 01.09.2018
Predmet:	Boolean algebra Brain Computing time in‐memory computing Ion migration Logic Materials science Neural networks neuromorphic Ohm's Law Pattern recognition Reconfiguration resistive switching memory stateful logic Switches Switching theory stateful logic in-memory computing resistive switching memory neural networks neuromorphic
ISSN:	0935-9648, 1521-4095, 1521-4095
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Abstract	Brain‐inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial intelligent tasks. By adopting binary inputs/outputs, neural networks can be used to perform Boolean logic operations, thus potentially surpassing complementary metal–oxide–semiconductor logic in terms of area efficiency, execution time, and computing parallelism. Here, the concept of stateful neural networks consisting of resistive switches, which can perform all logic functions with the same network topology, is introduced. The neural network relies on physical computing according to Ohm's law, Kirchhoff 's law, and the ionic migration within an output switch serving as the highly nonlinear activation function. The input and output are nonvolatile resistance states of the devices, thus enabling stateful and cascadable logic operations. Applied voltages provide the synaptic weights, which enable the convenient reconfiguration of the same circuit to serve various logic functions. The neural network can solve all two‐input logic operations with just one step, except for the exclusive‐OR (XOR) needing two sequential steps. 1‐bit full adder operation is shown to take place with just two steps and five resistive switches, thus highlighting the high efficiencies of space, time, and energy of logic computing with the stateful neural network. The concept of a stateful neural network is introduced based on a resistive memory circuit. Thanks to the universality and flexibility of the neural network, the circuit enables one‐step operation for all linearly separable logic functions, thus extremely reducing the numbers of computing steps and devices for stateful logic computing, for instance, two steps and five devices for the 1‐bit full adder.
AbstractList	Brain-inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial intelligent tasks. By adopting binary inputs/outputs, neural networks can be used to perform Boolean logic operations, thus potentially surpassing complementary metal-oxide-semiconductor logic in terms of area efficiency, execution time, and computing parallelism. Here, the concept of stateful neural networks consisting of resistive switches, which can perform all logic functions with the same network topology, is introduced. The neural network relies on physical computing according to Ohm's law, Kirchhoff 's law, and the ionic migration within an output switch serving as the highly nonlinear activation function. The input and output are nonvolatile resistance states of the devices, thus enabling stateful and cascadable logic operations. Applied voltages provide the synaptic weights, which enable the convenient reconfiguration of the same circuit to serve various logic functions. The neural network can solve all two-input logic operations with just one step, except for the exclusive-OR (XOR) needing two sequential steps. 1-bit full adder operation is shown to take place with just two steps and five resistive switches, thus highlighting the high efficiencies of space, time, and energy of logic computing with the stateful neural network. Brain-inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial intelligent tasks. By adopting binary inputs/outputs, neural networks can be used to perform Boolean logic operations, thus potentially surpassing complementary metal-oxide-semiconductor logic in terms of area efficiency, execution time, and computing parallelism. Here, the concept of stateful neural networks consisting of resistive switches, which can perform all logic functions with the same network topology, is introduced. The neural network relies on physical computing according to Ohm's law, Kirchhoff 's law, and the ionic migration within an output switch serving as the highly nonlinear activation function. The input and output are nonvolatile resistance states of the devices, thus enabling stateful and cascadable logic operations. Applied voltages provide the synaptic weights, which enable the convenient reconfiguration of the same circuit to serve various logic functions. The neural network can solve all two-input logic operations with just one step, except for the exclusive-OR (XOR) needing two sequential steps. 1-bit full adder operation is shown to take place with just two steps and five resistive switches, thus highlighting the high efficiencies of space, time, and energy of logic computing with the stateful neural network.Brain-inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial intelligent tasks. By adopting binary inputs/outputs, neural networks can be used to perform Boolean logic operations, thus potentially surpassing complementary metal-oxide-semiconductor logic in terms of area efficiency, execution time, and computing parallelism. Here, the concept of stateful neural networks consisting of resistive switches, which can perform all logic functions with the same network topology, is introduced. The neural network relies on physical computing according to Ohm's law, Kirchhoff 's law, and the ionic migration within an output switch serving as the highly nonlinear activation function. The input and output are nonvolatile resistance states of the devices, thus enabling stateful and cascadable logic operations. Applied voltages provide the synaptic weights, which enable the convenient reconfiguration of the same circuit to serve various logic functions. The neural network can solve all two-input logic operations with just one step, except for the exclusive-OR (XOR) needing two sequential steps. 1-bit full adder operation is shown to take place with just two steps and five resistive switches, thus highlighting the high efficiencies of space, time, and energy of logic computing with the stateful neural network. Brain‐inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial intelligent tasks. By adopting binary inputs/outputs, neural networks can be used to perform Boolean logic operations, thus potentially surpassing complementary metal–oxide–semiconductor logic in terms of area efficiency, execution time, and computing parallelism. Here, the concept of stateful neural networks consisting of resistive switches, which can perform all logic functions with the same network topology, is introduced. The neural network relies on physical computing according to Ohm's law, Kirchhoff 's law, and the ionic migration within an output switch serving as the highly nonlinear activation function. The input and output are nonvolatile resistance states of the devices, thus enabling stateful and cascadable logic operations. Applied voltages provide the synaptic weights, which enable the convenient reconfiguration of the same circuit to serve various logic functions. The neural network can solve all two‐input logic operations with just one step, except for the exclusive‐OR (XOR) needing two sequential steps. 1‐bit full adder operation is shown to take place with just two steps and five resistive switches, thus highlighting the high efficiencies of space, time, and energy of logic computing with the stateful neural network. The concept of a stateful neural network is introduced based on a resistive memory circuit. Thanks to the universality and flexibility of the neural network, the circuit enables one‐step operation for all linearly separable logic functions, thus extremely reducing the numbers of computing steps and devices for stateful logic computing, for instance, two steps and five devices for the 1‐bit full adder.
Author	Sun, Zhong Ielmini, Daniele Bricalli, Alessandro Ambrosi, Elia
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/30079525$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1007/BF02478259 10.1088/1361-6463/aa9646 10.1038/nnano.2017.83 10.1109/TVLSI.2002.808446 10.1038/530144a 10.1073/pnas.1407633111 10.1109/TNN.2003.816365 10.1002/adfm.201500865 10.1038/s41563-017-0001-5 10.1109/TED.2014.2325531 10.1038/nnano.2015.29 10.1007/978-3-642-61068-4 10.1002/adma.201204572 10.1002/adma.201602418 10.1109/TED.2015.2423001 10.1021/nn405827t 10.1002/adma.201203680 10.1109/TED.2015.2422999 10.1038/nmat3510 10.1038/nature14441 10.1109/TED.2011.2167513 10.1038/srep01680 10.1109/TED.2012.2202320 10.1038/nature08940 10.1109/JPROC.2013.2252317 10.1109/TED.2014.2330200 10.1109/T-C.1973.223685 10.1038/srep15467 10.1038/nnano.2016.70 10.1007/s12274-016-1260-1 10.1088/0268-1242/31/6/063002 10.1063/1.4852995 10.1038/nature06932 10.1002/adma.201301940 10.1038/s41928-018-0092-2 10.1038/s41928-017-0002-z 10.1109/TVLSI.2013.2282132 10.1109/TNNLS.2016.2547842
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References	1943; 5 2013; 3 2013; 25 2015; 5 2012 2015; 521 2010; 464 2002; 10 2015; 10 2013; 101 2009 2016; 31 2003; 14 2008 1996 2011; 58 2012; 59 2014; 111 2014; 61 2014; 22 2016; 11 2017; 50 2015; 25 2018; 17 1973; C‐22 2015; 62 2018; 1 2013; 12 2017; 12 2016; 530 2013; 114 2017 2015 2014 2008; 453 2016; 28 2014; 8 1969 2016; 9 e_1_2_5_27_1 e_1_2_5_28_1 e_1_2_5_25_1 e_1_2_5_23_1 e_1_2_5_24_1 e_1_2_5_45_1 e_1_2_5_21_1 Lehtonen E. (e_1_2_5_32_1) 2009 e_1_2_5_44_1 e_1_2_5_43_1 Chen B. (e_1_2_5_7_1) 2015 e_1_2_5_29_1 e_1_2_5_42_1 e_1_2_5_20_1 e_1_2_5_41_1 e_1_2_5_40_1 e_1_2_5_15_1 e_1_2_5_38_1 e_1_2_5_14_1 e_1_2_5_17_1 e_1_2_5_36_1 e_1_2_5_9_1 e_1_2_5_16_1 e_1_2_5_37_1 e_1_2_5_8_1 e_1_2_5_11_1 e_1_2_5_34_1 e_1_2_5_10_1 e_1_2_5_35_1 e_1_2_5_6_1 e_1_2_5_13_1 Waser R. (e_1_2_5_26_1) 2012 Reuben J. (e_1_2_5_31_1) 2017 e_1_2_5_5_1 e_1_2_5_12_1 e_1_2_5_33_1 e_1_2_5_4_1 e_1_2_5_3_1 e_1_2_5_1_1 e_1_2_5_19_1 e_1_2_5_18_1 Minsky M. L. (e_1_2_5_22_1) 1969 Horowitz M. (e_1_2_5_2_1) 2014 e_1_2_5_30_1 Pradhan D. (e_1_2_5_39_1) 1996 Lee H. Y. (e_1_2_5_46_1) 2008
References_xml	– volume: 3 start-page: 1680 year: 2013 publication-title: Sci. Rep. – start-page: 1 year: 2017 – volume: 10 start-page: 191 year: 2015 publication-title: Nat. Nanotechnol. – volume: 58 start-page: 4309 year: 2011 publication-title: IEEE Trans. Electron Devices – volume: 8 start-page: 2369 year: 2014 publication-title: ACS Nano – volume: 530 start-page: 144 year: 2016 publication-title: Nature – volume: 25 start-page: 1987 year: 2013 publication-title: Adv. Mater. – volume: 453 start-page: 80 year: 2008 publication-title: Nature – volume: 5 start-page: 15467 year: 2015 publication-title: Sci. Rep. – start-page: 1 year: 2008 publication-title: IEDM Tech. Dig. – volume: 14 start-page: 1217 year: 2003 publication-title: IEEE Trans. Neural Netw. – volume: 9 start-page: 3914 year: 2016 publication-title: Nano Res. – volume: 5 start-page: 115 year: 1943 publication-title: Bull. Math. Biophys. – volume: 31 start-page: 063002 year: 2016 publication-title: Semicond. Sci. Technol. – volume: 22 start-page: 2054 year: 2014 publication-title: IEEE Trans. Very Large Scale Integr. Syst. – year: 1996 – volume: 111 start-page: 13272 year: 2014 publication-title: Proc. Natl. Acad. Sci. USA – volume: 10 start-page: 806 year: 2002 publication-title: IEEE Trans. Very Large Scale Integr. (VLSI) Syst. – volume: 61 start-page: 2912 year: 2014 publication-title: IEEE Trans. Electron Devices – volume: 25 start-page: 1774 year: 2013 publication-title: Adv. Mater. – start-page: 33 year: 2009 – year: 2012 – volume: 59 start-page: 2468 year: 2012 publication-title: IEEE Trans. Electron Devices – volume: 464 start-page: 873 year: 2010 publication-title: Nature – volume: 1 start-page: 333 year: 2018 publication-title: Nat. Electron. – volume: 12 start-page: 784 year: 2017 publication-title: Nat. Nanotechnol. – volume: 61 start-page: 2378 year: 2014 publication-title: IEEE Trans. Electron Devices – volume: 25 start-page: 6414 year: 2015 publication-title: Adv. Funct. Mater. – volume: 28 start-page: 9758 year: 2016 publication-title: Adv. Mater. – volume: C‐22 start-page: 197 year: 1973 publication-title: IEEE Trans. Comput. – volume: 521 start-page: 61 year: 2015 publication-title: Nature – year: 1969 – volume: 12 start-page: 114 year: 2013 publication-title: Nat. Mater. – volume: 62 start-page: 1831 year: 2015 publication-title: IEEE Trans. Electron Dev. – volume: 101 start-page: 2498 year: 2013 publication-title: Proc. IEEE – volume: 50 start-page: 505102 year: 2017 publication-title: J. Phys. D: Appl. Phys. – volume: 25 start-page: 5975 year: 2013 publication-title: Adv. Mater. – volume: 114 start-page: 234503 year: 2013 publication-title: J. Appl. Phys. – volume: 28 start-page: 1734 year: 2016 publication-title: IEEE Trans. Neural Netw. Learn. Syst. – start-page: 10 year: 2014 – volume: 1 start-page: 52 year: 2018 publication-title: Nat. Electron. – volume: 11 start-page: 693 year: 2016 publication-title: Nat. Nanotechnol. – volume: 62 start-page: 1839 year: 2015 publication-title: IEEE Trans. Electron Devices – start-page: 17.5.1 year: 2015 publication-title: IEDM Tech. Dig. – volume: 17 start-page: 335 year: 2018 publication-title: Nat. Mater. – ident: e_1_2_5_15_1 doi: 10.1007/BF02478259 – ident: e_1_2_5_34_1 doi: 10.1088/1361-6463/aa9646 – ident: e_1_2_5_28_1 doi: 10.1038/nnano.2017.83 – ident: e_1_2_5_23_1 doi: 10.1109/TVLSI.2002.808446 – ident: e_1_2_5_1_1 doi: 10.1038/530144a – ident: e_1_2_5_12_1 doi: 10.1073/pnas.1407633111 – ident: e_1_2_5_16_1 doi: 10.1109/TNN.2003.816365 – ident: e_1_2_5_29_1 doi: 10.1002/adfm.201500865 – ident: e_1_2_5_41_1 doi: 10.1038/s41563-017-0001-5 – ident: e_1_2_5_21_1 doi: 10.1109/TED.2014.2325531 – ident: e_1_2_5_3_1 doi: 10.1038/nnano.2015.29 – ident: e_1_2_5_14_1 doi: 10.1007/978-3-642-61068-4 – ident: e_1_2_5_40_1 doi: 10.1002/adma.201204572 – ident: e_1_2_5_9_1 doi: 10.1002/adma.201602418 – ident: e_1_2_5_27_1 doi: 10.1109/TED.2015.2423001 – ident: e_1_2_5_37_1 doi: 10.1021/nn405827t – start-page: 1 volume-title: 27th Int. Symp. Power and Timing Modeling, Optimization and Simulation (PATMOS) year: 2017 ident: e_1_2_5_31_1 – ident: e_1_2_5_45_1 doi: 10.1002/adma.201203680 – volume-title: Nanoelectronics and Information Technology year: 2012 ident: e_1_2_5_26_1 – ident: e_1_2_5_8_1 doi: 10.1109/TED.2015.2422999 – ident: e_1_2_5_18_1 doi: 10.1038/nmat3510 – ident: e_1_2_5_44_1 doi: 10.1038/nature14441 – start-page: 1 year: 2008 ident: e_1_2_5_46_1 publication-title: IEDM Tech. Dig. – ident: e_1_2_5_20_1 doi: 10.1109/TED.2011.2167513 – ident: e_1_2_5_36_1 doi: 10.1038/srep01680 – ident: e_1_2_5_35_1 doi: 10.1109/TED.2012.2202320 – ident: e_1_2_5_5_1 doi: 10.1038/nature08940 – volume-title: Perceptrons year: 1969 ident: e_1_2_5_22_1 – ident: e_1_2_5_24_1 doi: 10.1109/JPROC.2013.2252317 – ident: e_1_2_5_38_1 doi: 10.1109/TED.2014.2330200 – start-page: 10 volume-title: Int. Solid‐State Circuits Conf. Dig. Tech. Papers year: 2014 ident: e_1_2_5_2_1 – ident: e_1_2_5_25_1 doi: 10.1109/T-C.1973.223685 – start-page: 17.5.1 year: 2015 ident: e_1_2_5_7_1 publication-title: IEDM Tech. Dig. – volume-title: Fault‐Tolerant Computer System Design year: 1996 ident: e_1_2_5_39_1 – ident: e_1_2_5_30_1 doi: 10.1038/srep15467 – ident: e_1_2_5_19_1 doi: 10.1038/nnano.2016.70 – ident: e_1_2_5_33_1 doi: 10.1007/s12274-016-1260-1 – start-page: 33 volume-title: Proc. IEEE/ACM Int. Symp. Nanoscale Architectures year: 2009 ident: e_1_2_5_32_1 – ident: e_1_2_5_13_1 doi: 10.1088/0268-1242/31/6/063002 – ident: e_1_2_5_11_1 doi: 10.1063/1.4852995 – ident: e_1_2_5_4_1 doi: 10.1038/nature06932 – ident: e_1_2_5_10_1 doi: 10.1002/adma.201301940 – ident: e_1_2_5_42_1 doi: 10.1038/s41928-018-0092-2 – ident: e_1_2_5_43_1 doi: 10.1038/s41928-017-0002-z – ident: e_1_2_5_6_1 doi: 10.1109/TVLSI.2013.2282132 – ident: e_1_2_5_17_1 doi: 10.1109/TNNLS.2016.2547842
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Snippet	Brain‐inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial... Brain-inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial...
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SubjectTerms	Boolean algebra Brain Computing time in‐memory computing Ion migration Logic Materials science Neural networks neuromorphic Ohm's Law Pattern recognition Reconfiguration resistive switching memory stateful logic Switches Switching theory
Title	Logic Computing with Stateful Neural Networks of Resistive Switches
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