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BEGIN:VEVENT
DTSTAMP:20181221T160729Z
LOCATION:Exhibit Hall B
DTSTART;TZID=America/Chicago:20181113T111500
DTEND;TZID=America/Chicago:20181113T120000
UID:submissions.supercomputing.org_SC18_sess224_inv103@linklings.com
SUMMARY:Brain-Inspired Massively-Parallel Computing
DESCRIPTION:Invited Talk\nTech Program Reg Pass, Exhibits Reg Pass\n\nBrai
 n-Inspired Massively-Parallel Computing\n\nFurber\n\nNeuromorphic computin
 g, that is, computing based upon brain-like principles - can be traced bac
 k to the pioneering work of Carver Mead in the 1980s. Academic research in
 to neuromorphic systems has continued since then in various forms, includi
 ng analog, digital and hybrid systems, primarily with the objective of imp
 roving understanding of information processing in the brain. More recently
 , industrial neuromorphic systems have emerged - first the IBM TrueNorth, 
 and then the Intel Loihi - with a greater focus on practical applications.
  In parallel, the last decade has seen an explosion of interest in less br
 ain-like, though still brain-inspired, artificial neural networks in machi
 ne learning applications that have, for example, placed high-quality speec
 h recognition systems into everyday consumer use. However, these artificia
 l neural networks consume significant computer and electrical power, parti
 cularly during training, and there is strong interest in bringing these re
 quirements down and in enabling continuous on-line learning to take place 
 in self-contained, mobile configurations. There is a growing expectation, 
 so far unsubstantiated by compelling evidence, that neuromorphic technolog
 ies will have a role to play in delivering these efficiency gains. The Spi
 NNaker (Spiking Neural Network Architecture) platform is an example of a h
 ighly flexible digital neuromorphic platform, based upon a massively-paral
 lel configuration of small processors with a bespoke interconnect fabric d
 esigned to support the very high connectivity of biological neural nets in
  real-time models. Although designed primarily to support brain science, i
 t can also be used to explore more applications-oriented research.
URL:https://sc18.supercomputing.org/presentation/?id=inv103&sess=sess224
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