A million spiking-neuron integrated circuit with a scalable communication network and interface

Paul Merolla(IBM Research - Almaden), John V. Arthur(IBM Research - Almaden), Rodrigo Alvarez-Icaza(IBM Research - Almaden), Andrew S. Cassidy(IBM Research - Almaden), Jun Sawada(IBM Research - Austin), Filipp Akopyan(IBM Research - Almaden), Bryan L. Jackson(IBM Research - Almaden), Nabil Imam(Cornell University), Guo Chen(IBM (United States)), Yutaka Nakamura(Toyo Engineering (Japan)), Bernard Brezzo(IBM (United States)), Ivan Vo(IBM Research - Austin), Steven K. Esser(IBM Research - Almaden), Rathinakumar Appuswamy(IBM Research - Almaden), Brian Taba(IBM Research - Almaden), Arnon Amir(IBM Research - Almaden), Myron Flickner(IBM Research - Almaden), W. P. Risk(IBM Research - Almaden), Rajit Manohar(Cornell University), Dharmendra S. Modha(IBM Research - Almaden)
Science
August 7, 2014
Cited by 4,057

Abstract

Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.


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