Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks

The human brain can perform advanced computing tasks, such as learning, recognition, and cognition, with extremely low power consumption and low frequency of neuronal spiking. This is attributed to the highly-parallel and the event-driven scheme of computation, where energy is used only when and whe...

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Vydané v:	Microelectronic engineering Ročník 190; s. 44 - 53
Hlavný autor:	Ielmini, Daniele
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 15.04.2018 Elsevier BV
Predmet:	Brain Cognition Computation Computer memory Deep learning Electronic devices Human performance Memory Memory devices Memristor Multilevel storage Nanotechnology devices Neural networks Neuromorphic engineering Pattern recognition Plastic properties Power consumption Resistive switching memory (RRAM) Scalability Spike-timing dependent plasticity (STDP) Switching theory Synapses Multilevel storage Deep learning Spike-timing dependent plasticity (STDP) Memristor Neuromorphic engineering Resistive switching memory (RRAM)
ISSN:	0167-9317, 1873-5568
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