Mitigating multiple single-event upsets during deep neural network inference using fault-aware training

Over the past decade deep neural networks (DNNs) have made remarkable advancements in terms of performance. Special hardware accelerators, designed to optimise the execution of these algorithms, have played a crucial role in this progress. However, their robustness remains a concern for safetycritical operations, especially when deployed in environments that contain high levels of radiation. In these harsh environments, accelerators are susceptible to single-event upsets (SEUs), which can lead to bit-flips causing numerical errors or even a system crash. One approach to mitigate SEUs is radiation hardening by design (RHBD).