BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250822T115805Z
LOCATION:Room 6.0D13
DTSTART;TZID=Europe/Stockholm:20250616T153000
DTEND;TZID=Europe/Stockholm:20250616T160000
UID:submissions.pasc-conference.org_PASC25_sess136_msa148@linklings.com
SUMMARY:Accelerating AI-based Genome Analysis via Algorithm-Architecture C
 o-Design
DESCRIPTION:Can Firtina (ETH Zurich)\n\nAnalyzing genomic data provides cr
 itical insights for understanding and treating diseases, outbreak tracing,
  evolutionary studies, agriculture, and many other areas of the life scien
 ces and personalized medicine. Modern genome sequencing devices can rapidl
 y generate large amounts of genomic data at a low cost. However, genome an
 alysis is bottlenecked by the computational and data movement overheads of
  existing systems and algorithms, causing significant limitations in terms
  of speed, accuracy, application scope, and energy efficiency of the analy
 sis. In this talk, we will focus on substantially improving the speed and 
 energy efficiency of a computationally costly machine learning (ML) techni
 que used in many important genomics applications. We will introduce ApHMM,
  which resolves significant inefficiencies that make an expectation-maximi
 zation technique costly for profile Hidden Markov Models (pHMMs) on genera
 l-purpose processors. ApHMM achieves this by effectively co-designing both
  hardware and algorithm. As a result, ApHMM provides substantial improveme
 nts in performance (up to two orders of magnitude) and energy efficiency (
 up to three orders of magnitude) compared to CPUs and GPUs.\n\nDomain: Eng
 ineering, Life Sciences\n\nSession Chairs: Bertil Schmidt (Johannes Gutenb
 erg University Mainz), Gagandeep Singh (AMD), and Sriranjani Sitaraman (AM
 D)\n\n
END:VEVENT
END:VCALENDAR