Statheros: Compiler for Efficient Low-Precision Probabilistic Programming

Jacob Laurel, Rem Yang, Atharva Sehgal, Shubham Ugare, Sasa Misailovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As Edge and IoT computing devices process noisy data or make decisions in uncertain environments, they require frameworks for inexpensive, yet accurate probabilistic inference. Probabilistic programming has emerged as a powerful way for developers to write high-level programs, while abstracting away the implementation details of inference. However, the existing algorithms are slow and often assumed to require precise calculations. We present Statheros, the first compiler for low-level, fixed-point approximation of probabilistic programming. Statheros compiles 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. 5x (Arduino), 3. 8x (PocketBeagle), and 2. 2x (Raspberry Pi) faster than single-precision floating-point computation, with minimal accuracy loss.

Original languageEnglish (US)
Title of host publication2021 58th ACM/IEEE Design Automation Conference, DAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages787-792
Number of pages6
ISBN (Electronic)9781665432740
DOIs
StatePublished - Dec 5 2021
Event58th ACM/IEEE Design Automation Conference, DAC 2021 - San Francisco, United States
Duration: Dec 5 2021Dec 9 2021

Publication series

NameProceedings - Design Automation Conference
Volume2021-December
ISSN (Print)0738-100X

Conference

Conference58th ACM/IEEE Design Automation Conference, DAC 2021
Country/TerritoryUnited States
CitySan Francisco
Period12/5/2112/9/21

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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