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DTSTAMP:20211208T164409Z
LOCATION:3014
DTSTART;TZID=America/Los_Angeles:20211208T103000
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UID:dac_DAC 2021_sess156_RESEARCH340@linklings.com
SUMMARY:Statheros: Compiler for Efficient Low-Precision Probabilistic Prog
 ramming
DESCRIPTION:Research Manuscript\n\nStatheros: Compiler for Efficient Low-P
 recision Probabilistic Programming\n\nLaurel\n\nAs Edge and IoT computing 
 devices process noisy data or make decisions in uncertain environments, th
 ey require frameworks for inexpensive, yet accurate probabilistic inferenc
 e. \n\nWe present Statheros, the first compiler for low-level, fixed-point
  approximation of probabilistic programming. Statheros translates programs
  to fixed-point inference procedures and is able to determine the optimal 
 fixed-point type to use. We evaluate Statheros on 13 benchmarks and three 
 embedded platforms. The results show that Statheros generated code is 11.5
 x (Arduino), 3.8x (PocketBeagle), and 2.2x (Raspberry Pi) faster than sing
 le-precision floating-point computation, with minimal accuracy loss.\n\nTo
 pic: Design\n\nKeyword: Approximate Computing for AI/ML\n\nVirtual Program
 : Presented In-Person
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