Expanding the Horizons of Finite-Precision Analysis
Debasmita Lohar
Max Planck Institute for Software Systems
27 Mar 2024, 2:45 pm - 3:45 pm
Saarbrücken building E1 5, room 029
SWS Student Defense Talks - Thesis Defense
Finite-precision programs, prevalent in embedded systems, scientific computing,
and machine learning, inherently introduce numerical uncertainties stemming
from noises in the inputs and finite-precision errors. Furthermore,
implementing these programs on hardware necessitates a trade-off between
accuracy and efficiency. Therefore, it is crucial to ensure that numerical
uncertainties remain acceptably small and to optimize implementations for
accurate results tailored to specific applications. Existing analysis and
optimization techniques for finite-precision programs face challenges in
scalability and applicability to real-world scenarios. ...
Finite-precision programs, prevalent in embedded systems, scientific computing,
and machine learning, inherently introduce numerical uncertainties stemming
from noises in the inputs and finite-precision errors. Furthermore,
implementing these programs on hardware necessitates a trade-off between
accuracy and efficiency. Therefore, it is crucial to ensure that numerical
uncertainties remain acceptably small and to optimize implementations for
accurate results tailored to specific applications. Existing analysis and
optimization techniques for finite-precision programs face challenges in
scalability and applicability to real-world scenarios. In this work, we expand
the individual capabilities of these techniques by capturing the impact of
uncertain inputs on discrete decisions and roundoff errors, by scaling
floating-point verification for larger programs, and by specializing
optimization for feed-forward deep neural networks.
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