119 citations · 465 across the 38 of their papers we have counts for
8 papers · 1 filter
Logic Bug Detection and Localization Using Symbolic Quick Error Detection
Eshan Singh, David Lin, Clark Barrett +1
We present Symbolic Quick Error Detection (Symbolic QED), a structured approach for logic bug detection and localization which can be used both during pre-silicon design verificati…
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
Divya Gopinath, Guy Katz, Corina S. Pasareanu +1
Deep neural networks have become widely used, obtaining remarkable results in domains such as computer vision, speech recognition, natural language processing, audio recognition, s…
Towards Proving the Adversarial Robustness of Deep Neural Networks
Guy Katz, Clark Barrett, David L. Dill +2
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficul…
Provably Minimally-Distorted Adversarial Examples
Nicholas Carlini, Guy Katz, Clark Barrett +1
The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclas…
E-QED: Electrical Bug Localization During Post-Silicon Validation Enabled by Quick Error Detection and Formal Methods
Eshan Singh, Clark Barrett, Subhasish Mitra
During post-silicon validation, manufactured integrated circuits are extensively tested in actual system environments to detect design bugs. Bug localization involves identificatio…
Constraint Solving for Finite Model Finding in SMT Solvers
Andrew Reynolds, Cesare Tinelli, Clark Barrett
SMT solvers have been used successfully as reasoning engines for automated verification and other applications based on automated reasoning. Current techniques for dealing with qua…
Reasoning with Finite Sets and Cardinality Constraints in SMT
Kshitij Bansal, Clark Barrett, Andrew Reynolds +1
We consider the problem of deciding the satisfiability of quantifier-free formulas in the theory of finite sets with cardinality constraints. Sets are a common high-level data stru…
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz, Clark Barrett, David Dill +2
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical syst…