OPUS Lab leading new interdisciplinary NSF project on neural network verification with probabilistic bits
OPUS Lab in collaboration with Northwestern University and the Zheng Zhang Lab at UCSB has received NSF funding to investigate the neural network verification problem with probabilistic computers.
This project addresses the critical need for verifying the safety and reliability of deep neural networks (DNNs) used in various applications, such as autonomous driving, aircraft control, and consumer products like smartphones. As the demand for computational power in artificial intelligence continues to grow, this project explores innovative domain-specific architectures (DSAs) such as quantum and probabilistic computing platforms as potential solutions to the verification problem. The research's significance lies in ensuring the correct functioning of DNNs when exposed to perturbations or attacks, with the goal of benefiting society by enhancing the safety and trustworthiness of Artificial Intelligence (AI)-driven technologies. This interdisciplinary project will not only advance the field of DNN verification and energy-efficient domain-specific computers but also support education and workforce development, increasing diversity and collaboration between academia and industry through targeted activities.
