Artículo
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
Autor/es | Camuñas Mesa, Luis Alejandro
Linares Barranco, Bernabé Serrano Gotarredona, María Teresa |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2019 |
Fecha de depósito | 2020-07-07 |
Publicado en |
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Resumen | Inspired by biology, neuromorphic systems have been trying to emulate the human brain for
decades, taking advantage of its massive parallelism and sparse information coding. Recently, several
large-scale hardware projects ... Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems. |
Identificador del proyecto | 687299 ”NEURAM3”
824164 ”HERMES” TEC2015-63884-C2-1-P (COGNET) |
Cita | Camuñas Mesa, L.A., Linares Barranco, B. y Serrano Gotarredona, M.T. (2019). Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations. Materials, 12 (17), 2745-. |
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