SCI-FI: a Smart, aCcurate and unIntrusive Fault-Injector for Deep Neural Networks

In recent years, the reliability of Deep Neural Networks (DNN) has become the focus of an increasing number of research activities. In particular, researchers have focused on understanding how a DNN behaves when the underlying hardware is affected by a fault. This is a challenging task: slight changes in a network architecture can significantly impact how the network reacts to faults. There are several approaches to simulate the behaviour of a faulty network: the most accurate one is to perform low-level fault simulations. Nonetheless, this task is very time-consuming and costly to be implemented. Even though the injection time can be reduced by injecting faults at the application level, for sufficiently large networks, this time is still very high, requiring weeks to complete a single simulation. This work aims at providing a fast and accurate solution for injecting software-level faults in a DNN that is independent of its architecture and does not require any modification to its structure. For this reason, this paper introduces SCI-FI, a Smart, aCcurate and unIntrusive Fault-Injector. SCI-FI smartly reduces the fault injection time required for a complete fault simulation of the network by taking advantage of two fundamental mechanisms: Fault Dropping and Delayed Start. Experimental results from various ResNet, DenseNet and EfficientNet architectures targeting the CIFAR-10 and ImageNet datasets show that combining these techniques drastically reduces the simulation time, which can last up to 70% less.