Software Open Access

Scalable Polyhedral Verification of Recurrent Neural Networks

Ryou, Wonryong; Chen, Jiyau; Mislav, Balunovic; Singh, Gagandeep; Dan, Andrei-Marian; Vechev, Martin

We present a scalable and precise verifier for recurrent neural networks, called Prover. Prover relies on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision and speech classification beyond the reach of prior work.

Please follow the readme.md located in the home directory. The system requires:

GPU available environment (not necessary but highly recommended)

Preliminary information for the VM:
1. List of essential files and directory
* /home/cav2021-paper187/readme.md: includes detailed instructions about how to prepare and run the artifact.
* /home/cav2021-paper187/CAV2021_paper_187.pdf: the accepted paper pdf.
* /home/cav2021-paper187/artifact/: the artifact codes and necessaries.
2. Recommended resource allocaiton
* Memory: 4GB
* Disk: 20GB because of the dataset
* GPU available environment
3. Credentials
* ID: cav2021-paper187
* PW: 2021-187-prover

Files (2.4 GB)
Name Size
CAV2021_Paper187.ova
md5:1877ebabf5191d78199ebfd5411a0ee3
2.4 GB
165
16
views