TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years

Autor:	Peter J. Carlson, Michael DeBole, John V. Arthur, Myron D. Flickner, Scott Lekuch, Jeffrey L. McKinstry, Andrew S. Cassidy, Michael Mastro, Jeff Kusnitz, Brian Taba, Carmelo di Nolfo, Rathinakumar Appuswamy, Jun Sawada, Steven K. Esser, Pallab Datta, Brent Paulovicks, Klamo Jennifer, Kai Schleupen, Kevin L. Holland, Arnon Amir, Guillaume J. Garreau, Filipp Akopyan, Dharmendra S. Modha, Benjamin G. Shaw, Alexander Andreopoulos, Tapan K. Nayak, Carlos Tadeo Ortega Otero, William P. Risk
Rok vydání:	2019
Předmět:	Random access memory General Computer Science Artificial neural network Computer science 020206 networking & telecommunications 02 engineering and technology Parallel computing TrueNorth Synapse 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering IBM Massively parallel 030217 neurology & neurosurgery
Zdroj:	Computer. 52:20-29
ISSN:	1558-0814 0018-9162
Popis:	IBM's brain-inspired processor is a massively parallel neural network inference engine containing 1 million spiking neurons and 256 million low-precision synapses. Now, after a decade of fundamental research spanning neuroscience, architecture, chips, systems, software, and algorithms, IBM has delivered the largest neurosynaptic computer ever built.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::49b898399142d35f409e1c3c3bdb2c8b https://doi.org/10.1109/mc.2019.2903009 Zobrazit plný text záznamu