REliable hardware for TRUSTworthy artificial INtelliGence – RE-TRUSTING

Artificial Intelligence (AI) algorithms, and in particular Deep Neural Networks (DNNs), typically run in the cloud on clusters of CPUs and GPUs. To be able to run DNNs algorithms out of the cloud and onto distributed Internet-of-Things (IoT) devices, customized HardWare platforms for Artificial Intelligence (HW-AI) are required. However, similar to traditional computing hardware, HW-AI is subject to hardware faults, occurring due to manufacturing faults, component aging and environmental perturbations. Although HW-AI comes with some inherent fault resilience, faults can still occur after the training phase and can seriously affect DNN inference running on the HW-AI. As a result, DNN prediction failures can appear, seriously affecting the application execution. Furthermore, if the hardware is compromised, then any attempt to explain AI decisions risks to be inconclusive or misleading. One of the overlooked aspects in the state-of-the-art is the impact that hardware faults can have in the application execution and the decisions of HW-AI. This impact is of significant importance, especially when HW-AI is deployed in safety-critical and mission-critical applications, such as robotics, aerospace, smart healthcare, and autonomous driving. RE-TRUSTING is the first project to include the impact of HW-AI reliability on the safety, trust, and explainability of AI decisions. Typical reliability approaches, such as on-line testing and hardware redundancy, or even retraining, are less appropriate for HW-AI due to prohibited area and power overheads. Indeed, DNNs are large architectures with important memory requirements, coming along with an immense training set. RE-TRUSTING will address these limitations by exploiting the particularities of HW-AI architectures to develop low-cost and efficient reliability strategies. To achieve that, RE-TRUSTING will develop fault models and perform a failure analysis of HW-AI with the aim to study their vulnerability towards explaining the HW-AI. Explainable HW-AI signifies reassuring that the HW-AI is fault-free, thus neither compromising nor biasing the AI decision-making. In this regard, RE-TRUSTING aims at bringing confidence into the AI decision-making by explaining the hardware on which AI algorithms are being executed.