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Machine Learning is among the greatest advancements in computer science and engineering and is today used to classify or detect objects, a key feature in autonomous vehicles. Since neural networks are heavily used in safety-critical applications, such as automotive and aerospace, their reliability must be paramount. However, the reliability evaluation of neural network systems is extremely challenging due to the complexity of the software, which is composed of hundreds of layers, and of the underlying hardware, typically a parallel device or an embedded accelerator. This paper reviews fundamental concepts of Artificial Intelligence, Deep Neural Network, and parallel computing device reliability. Then, the reliability studies that consider the radiation effects in the hardware, their propagation through the computing architecture, and their final impact on the software output are summarized. A detailed survey of the available strategies to measure the sensitivity of neural network frameworks and to observe fault propagation is given, together with a summary of the data obtained so far. Finally, a discussion on how to use the experimental evaluation to design effective and efficient hardening solutions for Artificial Neural Networks is provided. The available hardening solutions are critically reviewed, highlighting their benefits and drawbacks.
Paolo Rech (Thu,) studied this question.