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DESCRIPTION:Neuromorphic Computing promises orders of magnitude improvement
  in energy efficiency compared to the traditional von Neumann computing pa
 radigm. The goal is to develop an adaptive\, fault-tolerant\, low-footprin
 t\, fast\, low-energy intelligent system by learning and emulating brain f
 unctionality which can be realized through innovation in different abstrac
 tion layers including material\, device\, circuit\, architecture\, and alg
 orithm. As the energy consumption in complex machine learning tasks keeps 
 increasing exponentially due to larger data sets and resource-constrained 
 edge devices becoming increasingly ubiquitous\, neuromorphic computing app
 roaches can be a viable alternative to a deep convolutional neural network
  that is dominating the field today. In this talk\, I introduce neuromorph
 ic computing\, outline a few representative examples from different layers
  of the design stack (devices\, circuits\, and algorithms) and conclude wi
 th a few important challenges and opportunities in this field.\n\nSpeaker(
 s): Md Sakib Hasan\, Ph.D.\, \n\nVirtual: https://events.vtools.ieee.org/m
 /350928
LOCATION:Virtual: https://events.vtools.ieee.org/m/350928
ORGANIZER:upalmahbub@yahoo.com
SEQUENCE:5
SUMMARY:Overview of Neuromorphic Computing: Challenges and Opportunities
URL;VALUE=URI:https://events.vtools.ieee.org/m/350928
X-ALT-DESC:Description: <br /><p><span style="font-weight: 400\;">Neuromorp
 hic Computing promises orders of magnitude improvement in energy efficienc
 y compared to the traditional von Neumann computing paradigm. The goal is 
 to develop an adaptive\, fault-tolerant\, low-footprint\, fast\, low-energ
 y intelligent system by learning and emulating brain functionality which c
 an be realized through innovation in different abstraction layers includin
 g material\, device\, circuit\, architecture\, and algorithm. As the energ
 y consumption in complex machine learning tasks keeps increasing exponenti
 ally due to larger data sets and resource-constrained edge devices becomin
 g increasingly ubiquitous\, neuromorphic computing approaches can be a via
 ble alternative to a deep convolutional neural network that is dominating 
 the field today. In this talk\, I introduce neuromorphic computing\, outli
 ne a few representative examples from different layers of the design stack
  (devices\, circuits\, and algorithms) and conclude with a few important c
 hallenges and opportunities in this field.</span></p>
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