NSF award 2551479 · Rutgers University New Brunswick

SHF: Efficient, Scalable and Certified Convex Hull Computation in High Dimensions for Verifying Neural Networks and Generating Correctly-rounded Math Libraries

Final report due

28 October 2030

in 1,471 days · The award ends on 30 June 2030.

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PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Jun 2030in 1341 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Oct 2030in 1471 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Oct 2030in 1471 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Oct 2030in 1471 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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So the final report is due by 28 October 2030.

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Award card

Award number: 2551479
Period of support: 1 Jul 2026 – 30 Jun 2030
Amount: $850,000
Awardee: Rutgers University New Brunswick, NEW Brunswick, NJ
Program: Software & Hardware Foundation
Directorate: CSE

Abstract

The growing adoption of neural networks in safety-critical domains mandates techniques for verifying their robustness, safety and consistency. Precisely encoding the architecture of the network into a first-order logic formula does not scale to large problem sizes due to the large number of non-linear activations and complex shapes of general activation functions. Thus, state-of-the-art verifiers use abstract…

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