SAS 2021
Sun 17 - Fri 22 October 2021 Chicago, Illinois, United States
co-located with SPLASH 2021
Sun 17 Oct 2021 09:30 - 09:45 at Zurich B - Session 1A Chair(s): Cezara Drăgoi
Sun 17 Oct 2021 17:30 - 17:45 at Zurich B - Session 1A Chair(s): Kedar Namjoshi

Deep neural networks are an attractive tool for compressing the control policy lookup tables in systems such as the Airborne Collision Avoidance System (ACAS). It is vital to ensure the safety of such neural controllers via verification techniques. The problem of analyzing ACAS Xu networks has motivated many successful neural network verifiers. These verifiers typically analyze the internal computation of neural networks to decide whether a property regarding the input/output holds. The intrinsic complexity of neural network computation renders such verifiers slow to run and vulnerable to floating-point error.

This paper revisits the original problem of verifying ACAS Xu networks. The networks take low-dimensional sensory inputs with training data extracted from a lookup table. We propose to prepend an input quantization layer to the network. Quantization allows efficient verification via input state enumeration, whose complexity is bounded by the size of the quantization space. Quantization is equivalent to nearest-neighbor interpolation at run time, which has been shown to provide acceptable accuracy for ACAS in simulation. Moreover, our technique can deliver exact verification results immune to floating-point error if we directly enumerate the network outputs on the target inference implementation or on an accurate simulation of the target implementation.

Sun 17 Oct

Displayed time zone: Central Time (US & Canada) change

09:00 - 10:20
Session 1ASAS at Zurich B +8h
Chair(s): Cezara Drăgoi Inria / ENS / Informal Systems
09:00
15m
Talk
Accelerating Program Analyses in Datalog by Merging Library FactsVirtual
SAS
Yifan Chen Peking University, Chenyang Yang , Xin Zhang Peking University, Yingfei Xiong Peking University, Hao Tang Peking University, Xiaoyin Wang University of Texas at San Antonio, Lu Zhang Peking University
09:15
15m
Talk
Exploiting Verified Neural Networks via Floating Point Numerical ErrorVirtual
SAS
Kai Jia Massachusetts Institute of Technology, Martin C. Rinard
Pre-print
09:30
15m
Talk
Verifying Low-dimensional Input Neural Networks via Input QuantizationVirtual
SAS
Kai Jia Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology
Pre-print
09:45
15m
Talk
A Multi-Language Static Analysis of Python Programs with Native C ExtensionsVirtual
SAS
Raphaël Monat Sorbonne Université — LIP6, Abdelraouf Ouadjaout Sorbonne Université, Antoine Miné Sorbonne Université
Pre-print Media Attached
10:00
20m
Live Q&A
Session 1A Discussion, Questions and Answers Virtual
SAS

17:00 - 18:20
Session 1ASAS at Zurich B
Chair(s): Kedar Namjoshi Nokia Bell Labs
17:00
15m
Talk
Accelerating Program Analyses in Datalog by Merging Library FactsVirtual
SAS
Yifan Chen Peking University, Chenyang Yang , Xin Zhang Peking University, Yingfei Xiong Peking University, Hao Tang Peking University, Xiaoyin Wang University of Texas at San Antonio, Lu Zhang Peking University
17:15
15m
Talk
Exploiting Verified Neural Networks via Floating Point Numerical ErrorVirtual
SAS
Kai Jia Massachusetts Institute of Technology, Martin C. Rinard
Pre-print
17:30
15m
Talk
Verifying Low-dimensional Input Neural Networks via Input QuantizationVirtual
SAS
Kai Jia Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology
Pre-print
17:45
15m
Talk
A Multi-Language Static Analysis of Python Programs with Native C ExtensionsVirtual
SAS
Raphaël Monat Sorbonne Université — LIP6, Abdelraouf Ouadjaout Sorbonne Université, Antoine Miné Sorbonne Université
Pre-print Media Attached
18:00
20m
Live Q&A
Session 1A Discussion, Questions and Answers Virtual
SAS