This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/79003
Description
Title
Approximate Computing with Probabilistic Programs
Author(s)
Tomei, Matthew
Issue Date
2015-05
Keyword(s)
approximate computing
probabilistic programming
Abstract
Approximate computing involves relaxing program accuracy requirements to improve performance
or decrease energy consumption. Since program accuracy measures tend to be
non-deterministic due to multiple sources of uncertainty (e.g., inputs), it should be possible to reason about an approximate program as a probabilistic program. In this thesis, we
present a framework for reasoning about approximate programs as probabilistic programs.
This framework enables solving the problem of finding the optimal approximate program,
i.e. the approximate program that minimizes cost while providing statistical guarantees for
correctness, in a general setting. Treating approximate programs as probabilistic programs
also allows us to apply probabilistic programming tools to approximate computing. One
such tool allows us to decrease the time it takes to find an optimal approximate program.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
