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DTSTAMP:20240229T213536Z
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DTSTART;TZID=Canada/Eastern:20240229T130000
DTEND;TZID=Canada/Eastern:20240229T143000
DESCRIPTION:Abstract : Artificial intelligence (AI) powered by neural netwo
 rks has enabled applications in many fields (medicine\, finance\, autonomo
 us vehicles). Digital implementations of neural networks are limited in sp
 eed and energy efficiency. Neuromorphic photonics aims to build processors
  that use light and photonic device physics to mimic neurons and synapses 
 in the brain for distributed and parallel processing while offering sub-na
 nosecond latencies and extending the domain of AI and neuromorphic computi
 ng applications. We will discuss photonic neural networks enabled by CMOS-
 compatible silicon photonics. We will highlight applications that require 
 low latency and high bandwidth\, including wideband radio-frequency signal
  processing\, fiber-optic communications\, and nonlinear programming (solv
 ing optimization problems). We will briefly introduce a quantum photonic n
 eural network that can learn to act as near-perfect components of quantum 
 technologies and discuss the role of weak nonlinearities.\n\nSpeaker(s): P
 rof. Bhavin Shastri\, \n\nBldg: McConnell Engineering building\, \, Room M
 D 497\, 4th floor\, 817 Sherbrooke St W\, \, Montreal\, Quebec\, Canada\, 
 H3A 0C3
LOCATION:Bldg: McConnell Engineering building\, \, Room MD 497\, 4th floor\
 , 817 Sherbrooke St W\, \, Montreal\, Quebec\, Canada\, H3A 0C3
ORGANIZER:lawrence.chen@mcgill.ca
SEQUENCE:13
SUMMARY:Photonics for artificial intelligence and neuromorphic computing: c
 lassical to quantum
URL;VALUE=URI:https://events.vtools.ieee.org/m/401582
X-ALT-DESC:Description: <br /><p><strong>Abstract :</strong> &nbsp\;Artific
 ial intelligence (AI) powered by neural networks has enabled applications 
 in many fields (medicine\, finance\, autonomous vehicles). Digital impleme
 ntations of neural networks are limited in speed and energy efficiency. Ne
 uromorphic photonics aims to build processors that use light and photonic 
 device physics to mimic neurons and synapses in the brain for distributed 
 and parallel processing while offering sub-nanosecond latencies and extend
 ing the domain of AI and neuromorphic computing applications. We will disc
 uss photonic neural networks enabled by CMOS-compatible silicon photonics.
  We will highlight applications that require low latency and high bandwidt
 h\, including wideband radio-frequency signal processing\, fiber-optic com
 munications\, and nonlinear programming (solving optimization problems). W
 e will briefly introduce a quantum photonic neural network that can learn 
 to act as near-perfect components of quantum technologies and discuss the 
 role of weak nonlinearities.</p>
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