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Logic Computing with Stateful Neural Networks of Resistive Switches
Author(s) -
Sun Zhong,
Ambrosi Elia,
Bricalli Alessandro,
Ielmini Daniele
Publication year - 2018
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201802554
Subject(s) - stateful firewall , computer science , artificial neural network , memristor , xor gate , neuromorphic engineering , logic gate , topology (electrical circuits) , electronic engineering , artificial intelligence , electrical engineering , algorithm , computer network , engineering , network packet
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.