Neuromemristive Systems: Boosting Efficiency through Brain-Inspired Computing.

Neuromemristive systems (NMSs) are gaining traction as an alternative to conventional CMOS-based von Neumann systems because of their greater energy and area efficiency. A proposed NMS accelerator for machine-learning tasks reduced power dissipation by five orders of magnitude, relative to a multico...

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Detalles Bibliográficos
Publicado en:	Computer (00189162) Vol. 49; no. 10; pp. 56 - 65
Autores principales:	Merkel, Cory, Hasan, Raqibul, Soures, Nicholas, Kudithipudi, Dhireesha, Taha, Tarek, Agarwal, Sapan, Marinella, Matthew
Formato:	Artículo
Publicado:	IEEE Oct2016
Materias:	Machine learning Energy dissipation Microprocessors High performance computing Computer systems
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	Neuromemristive systems (NMSs) are gaining traction as an alternative to conventional CMOS-based von Neumann systems because of their greater energy and area efficiency. A proposed NMS accelerator for machine-learning tasks reduced power dissipation by five orders of magnitude, relative to a multicore reduced-instruction set computing processor.