OPTOXENSE, INC. — Department of Defense SBIR Phase I: N231-061

Description

Verification for deep neural networks and learning-enabled autonomous systems is critical for the safe deployment of artificial intelligence in the wild. Although significant efforts have been made in academia and industry with rigorous techniques and tools developed in this area, several grand challenges still need to be addressed for the applicability and usage of state-of-the-art techniques in real-world applications. optoXense is teaming with University of Nebraska - Lincoln to propose solutions for five key challenges in both open-loop and closed-loop verification, including 1) scalability, conservativeness, memory, and timing performance, 2) quantitative real-time verification, 3) intuitive user-friendly interface, and ROS-integrability, 4) handling temporal time-dependent properties, and 5) all-in-one demand. In Phase I, we aim to develop basic front-end and selected back-end features of the proposed tool to demonstrate feasibility. Dr. Tran of UNL has been developing the NNV (Neural Network Verification) framework for Deep Neural Networks and Learning-enabled CPS (Cited in N231-061 topic reference no. 9). The NNV tool is gaining great attention from industry and academia. It has been used for the DARPA Assured Autonomy project and is currently partially supported by Toyota Research and NSF. This proposal aims to improve on NNV by eliminating dependency on proprietary software. We aspire to deliver a tool that could become the dominant platform for neural network verification. This aspiration could be realized by a flexible architecture that could be extended by a community of fellow researchers and developers.