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UID:DSC-17893
DTSTART;TZID=Europe/Berlin:20210422T150000
SEQUENCE:1618870251
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20210422T160000
URL:https://www.dresden-science-calendar.de/calendar/de/detail/17893
LOCATION:TUD Andreas-Schubert-Bau\, Zellescher Weg 1901069 Dresden
SUMMARY:Berthold: Anne-Sophie Berthold (IKTP) &amp\; Nick Fritzsche (IKTP):
  Convolutional Neural Networks for processing of ATLAS Liquid Argon Calori
 meter signals with FPGAs
CLASS:PUBLIC
DESCRIPTION:Speaker: Anne-Sophie Berthold &amp\; Nick Fritzsche\nInstitute 
 of Speaker: IKTP\nTopics:\nPhysik\n Location:\n  Name: TUD Andreas-Schuber
 t-Bau ()\n  Street: Zellescher Weg 19\n  City: 01069 Dresden\n  Phone: \n 
  Fax: \nDescription: <p>Links:</p>  <ul>  <li>with ZIH login (https://self
 service.zih.tu-dresden.de/l/link.php?m=93332&amp\;p=4b85ed6a)</li>  <li>wi
 thout ZIH login (https://selfservice.zih.tu-dresden.de/link.php?m=93332&am
 p\;p=d39c7987)</li> </ul>  <p>Abstract:</p>  <p>Starting in 2027\, the enh
 anced performance of the High-Luminosity LHC<br /> will increase the numbe
 r of particle collisions in the ATLAS detector<br /> significantly. Since 
 up to 200 pile-up events will emerge within one<br /> bunch crossing\, one
  important part of the so-called Phase-II upgrade<br /> will be the proces
 sing of the Liquid-Argon Calorimeter signals. It has<br /> been shown that
  the conventional signal processing\, which applies an<br /> optimal filte
 ring algorithm\, will loose its performance due to the<br /> increase of o
 verlapping signals and a trigger scheme with trigger accept<br /> signals 
 in each LHC bunch crossing. That is why more sophisticated<br /> algorithm
 s such as neural networks come into focus. This talk will deal<br /> with 
 the development and performance of convolutional neural networks<br /> and
  their resource-optimized implementation on FPGAs.</p>
DTSTAMP:20260815T060322Z
CREATED:20210419T221051Z
LAST-MODIFIED:20210419T221051Z
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