The dAIEDGE Network of Excellence (NoE) seeks to strengthen and support the development of a dynamic European cutting-edge Artificial intelligence (AI) ecosystem under the umbrella of the European Lighthouse for AI, and to sustain the development of advanced AI. dAIEDGE fosters the exchange of ideas, concepts, and trends on cutting-edge next generation AI, creating links between ecosystem actors to help both the European Commission (EC) and the European Union (EU) and the peripheral AI constituency identify strategies for future developments in Europe. Our main objective is to advance Europe’s innovation and technology base by developing a comprehensive policy and governance approach to AI in order for the EU to become a world leader in innovation in the data economy and its applications.

International audience

We present a novel test generation algorithm for hardware accelerators of Spiking Neural Networks (SNNs). The algorithm is based on advanced optimization tailored for the spiking domain. It adaptively crafts input samples towards high coverage of hardware-level faults. Time-consuming fault simulation during test generation is circumvented by defining loss functions targeting the maximization of fault sensitisation and fault effect propagation to the output. Comparing the proposed algorithm to the existing ones on three benchmarks, it scales up for large SNN models, and it drastically reduces the test generation runtime from days to hours and the test duration from minutes to seconds. The resultant test input shows near perfect fault coverage and has a duration equivalent to a few dataset samples, thus, besides post-manufacturing testing, it is also suited for in-field testing.

As Spiking Neural Networks (SNNs) gain traction across various applications, understanding their security vulnerabilities becomes increasingly important. In this work, we focus on the adversarial attacks, which is perhaps the most concerning threat. An adversarial attack aims at finding a subtle input perturbation to fool the network’s decision-making. We propose two novel adversarial attack algorithms for SNNs: an input-specific attack that crafts adversarial samples from specific dataset inputs and a universal attack that generates a reusable patch capable of inducing misclassification across most inputs, thus offering practical feasibility for real-time deployment. The algorithms are gradient-based operating in the spiking domain proving to be effective across different evaluation metrics, such as adversarial accuracy, stealthiness, and generation time. Experimental results on two widely used neuromorphic vision datasets, NMNIST and IBM DVS Gesture, show that our proposed attacks surpass in all metrics all existing state-of-the-art methods. Additionally, we present the first demonstration of adversarial attack generation in the sound domain using the SHD dataset.