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DTSTAMP:20251105T001544Z
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DTSTART;TZID=America/Denver:20251028T120000
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DESCRIPTION:The brain is the perfect place to look for inspiration to devel
 op more efficient neural networks. The inner workings of our synapses and 
 neurons offer a glimpse at what the future of deep learning might look lik
 e. Our brains are constantly adapting\, our neurons processing all that we
  know\, mistakes we’ve made\, failed predictions—all working to antici
 pate what will happen next with incredible speed. Our brains are also amaz
 ingly efficient. Training large-scale neural networks can cost more than $
 100 million in energy expense\, yet the human brain does remarkably well o
 n a power budget of 20 watts.\n\nWe can apply the computational principles
  that underpin the brain\, and use them to engineer more efficient systems
  that adapt to ever changing environments. There is an interplay between n
 eural inspired algorithms\, how they can be deployed on low-power microele
 ctronics\, and how the brain provides a blueprint for this process.\n\nCo-
 sponsored by: CH04099 - Southeastern Michigan Chapter\, EMB\n\nSpeaker(s):
  Jason K. Eshraghian\, \n\nAgenda: \n12:00 Noon. Open of meeting and intro
 duction of Dr. Jason Eshraghian.\n\n12:10 - 12:50 pm. Presentation\n\n12:5
 0 - 1pm. Q&A\n\nVirtual: https://events.vtools.ieee.org/m/502843
LOCATION:Virtual: https://events.vtools.ieee.org/m/502843
ORGANIZER:jharrer@ieee.org
SEQUENCE:39
SUMMARY:Everything you always wanted to know about Neuromorphic Computing b
 ut were afraid to ask
URL;VALUE=URI:https://events.vtools.ieee.org/m/502843
X-ALT-DESC:Description: <br /><p class="MsoNormal">&nbsp\;</p>\n<p class="M
 soNormal">The brain is the perfect place to look for inspiration to develo
 p more efficient neural networks. The inner workings of our synapses and n
 eurons offer a glimpse at what the future of deep learning might look like
 . Our brains are constantly adapting\, our neurons processing all that we 
 know\, mistakes we&rsquo\;ve made\, failed predictions&mdash\;all working 
 to anticipate what will happen next with incredible speed. Our brains are 
 also amazingly efficient. Training large-scale neural networks can cost mo
 re than $100 million in energy expense\, yet the human brain does remarkab
 ly well on a power budget of 20 watts.</p>\n<p class="MsoNormal">We can ap
 ply the computational principles that underpin the brain\, and use them to
  engineer more efficient systems that adapt to ever changing environments.
  There is an interplay between neural inspired algorithms\, how they can b
 e deployed on low-power microelectronics\, and how the brain provides a bl
 ueprint for this process.</p><br /><br />Agenda: <br /><p>12:00 Noon.&nbsp
 \; Open of meeting and introduction of Dr. Jason Eshraghian.</p>\n<p>12:10
  - 12:50 pm.&nbsp\; Presentation</p>\n<p>12:50 - 1pm. Q&amp\;A</p>
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