# Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

## TL;DR

This paper introduces a novel CUDA-based symbolic verification framework for artificial neural networks, enabling the detection of adversarial cases and safety property violations in safety-critical applications.

## Contribution

It develops the first symbolic verification framework for CUDA-implemented ANNs using incremental bounded model checking, enhancing safety analysis capabilities.

## Key findings

- Successfully verified safety properties in ANNs
- Generated 28 adversarial cases in MLPs
- Demonstrated effectiveness of the framework in CUDA environments

## Abstract

Artificial Neural networks (ANNs) are powerful computing systems employed for various applications due to their versatility to generalize and to respond to unexpected inputs/patterns. However, implementations of ANNs for safety-critical systems might lead to failures, which are hardly predicted in the design phase since ANNs are highly parallel and their parameters are hardly interpretable. Here we develop and evaluate a novel symbolic software verification framework based on incremental bounded model checking (BMC) to check for adversarial cases and coverage methods in multi-layer perceptron (MLP). In particular, we further develop the efficient SMT-based Context-Bounded Model Checker for Graphical Processing Units (ESBMC-GPU) in order to ensure the reliability of certain safety properties in which safety-critical systems can fail and make incorrect decisions, thereby leading to unwanted material damage or even put lives in danger. This paper marks the first symbolic verification framework to reason over ANNs implemented in CUDA. Our experimental results show that our approach implemented in ESBMC-GPU can successfully verify safety properties and covering methods in ANNs and correctly generate 28 adversarial cases in MLPs.