BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20211208T164413Z
LOCATION:3014
DTSTART;TZID=America/Los_Angeles:20211209T135300
DTEND;TZID=America/Los_Angeles:20211209T141600
UID:dac_DAC 2021_sess118_RESEARCH1145@linklings.com
SUMMARY:CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Networ
 k Accelerator
DESCRIPTION:Research Manuscript\n\nCrossLight: A Cross-Layer Optimized Sil
 icon Photonic Neural Network Accelerator\n\nSunny\n\nDomain-specific neura
 l network accelerators have seen growing interest in recent years due to t
 heir improved energy efficiency and inference performance compared to CPUs
  and GPUs. In this paper, we propose a novel cross-layer optimized neural 
 network accelerator called CrossLight that leverages silicon photonics. Cr
 ossLight includes device-level engineering for resilience to process varia
 tions and thermal crosstalk, circuit-level tuning enhancements for inferen
 ce latency reduction, and architecture-level optimization to enable higher
  resolution, better energy-efficiency, and improved throughput. On average
 , CrossLight offers 9.5x lower energy-per-bit and 15.9x higher performance
 -per-watt at 16-bit resolution than state-of-the-art photonic deep learnin
 g accelerators.\n\nTopic: Design\n\nKeyword: AI/ML System Design\n\nVirtua
 l Program: Presented In-Person
END:VEVENT
END:VCALENDAR