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SUMMARY:3D Vertical Integration Technology for Fast Pattern Recognition
DTSTART;VALUE=DATE-TIME:20120505T090000Z
DTEND;VALUE=DATE-TIME:20120505T093000Z
DTSTAMP;VALUE=DATE-TIME:20130620T122542Z
UID:indico-contribution-6@cern.ch
DESCRIPTION:Speakers: LIU\, Tiehui Ted (Fermilab)\nHardware-based pattern 
 recognition for fast triggering on particle tracks has been successfully u
 sed in high-energy physics experiments for some time. The CDF Silicon Vert
 ex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The met
 hod used there\, developed in the 1990’s at Pisa\, is based on algorithm
 s that use a massively parallel associative memory architecture to identif
 y patterns efficiently at high speed. However\, due to much higher occupan
 cy and event rates at the LHC\, and the fact that the LHC detectors have a
  much larger number of channels in their tracking detectors\, there is an 
 enormous challenge in implementing fast pattern recognition for a track tr
 igger\, requiring about three orders of magnitude more associative memory 
 patterns than what was implemented in the original CDF SVT. Scaling of cur
 rent technologies is unlikely to satisfy the scientific needs of the futur
 e\, and investments in transformational new technologies need to be made. 
 As Moore’s law is approaching severe limitations\, it is expected that 3
 D Vertical Integration Technology will be the next scaling engine. More im
 portantly\, in certain cases\, the 3D technology also provides novel desig
 n opportunities that are simply not possible in 2D and this is the case fo
 r fast pattern recognition\, such as the associative memory approach. In t
 his talk\, we will present a new concept of using the emerging 3D vertical
  integration technology to significantly advance the state-of-the-art for 
 fast pattern recognition within and outside HEP. A R&D collaboration based
  on this concept is being developed and the status of this R&D project as 
 well as the future direction will be presented as well.\n\nhttp://indico.c
 ern.ch/contributionDisplay.py?contribId=6&sessionId=3&confId=154525
LOCATION:INFN Pisa
URL:http://indico.cern.ch/contributionDisplay.py?contribId=6&sessionId=3&c
 onfId=154525
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