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DTSTAMP:20240626T180034Z
LOCATION:3004\, 3rd Floor
DTSTART;TZID=America/Los_Angeles:20240625T133000
DTEND;TZID=America/Los_Angeles:20240625T134500
UID:dac_DAC 2024_sess168_RESEARCH849@linklings.com
SUMMARY:Massively Parallel AIG Resubstitution
DESCRIPTION:Research Manuscript\n\nYang Sun, Tianji Liu, Martin Wong, and 
 Evangeline Young (The Chinese University of Hong Kong)\n\nResubstitution i
 s a flexible algorithmic framework for circuit restructuring that has been
  incorporated into many high-effort logic optimization flows. It is thus i
 mportant to speed up resubstitution in order to obtain high-quality realiz
 ations of large-scale designs. This paper proposes a massively parallel AI
 G resubstitution algorithm targeting GPUs, with effective approaches to ad
 dressing cyclic dependencies and restructuring conflicts. Compared with AB
 C and mockturtle, our algorithm achieves 41.9x and 50.3x acceleration on a
 verage without quality degradation. When combining our resubstitution with
  other GPU algorithms, a GPU-based resyn2rs sequence obtains 46.4x speedup
  over ABC with 0.8% and 5.8% smaller area and delay respectively.\n\nTopic
 : EDA\n\nKeyword: RTL/Logic Level and High-level Synthesis\n\nSession Chai
 rs: Giovanni De Micheli (EPFL) and Cunxi Yu (University of Maryland)
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