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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Vydáno v:Advanced materials (Weinheim) Ročník 30; číslo 38; s. e1802554 - n/a
Hlavní autoři: Sun, Zhong, Ambrosi, Elia, Bricalli, Alessandro, Ielmini, Daniele
Médium: Journal Article
Jazyk:angličtina
Vydáno: Germany Wiley Subscription Services, Inc 01.09.2018
Témata:
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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Shrnutí: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.
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ISSN:0935-9648
1521-4095
1521-4095
DOI:10.1002/adma.201802554