Addressing Soft Error and Security Threats in DNNs Using Learning Driven Algorithmic Checks

The reliability of Deep Neural Networks (DNNs) is of great concern due to their widespread use in safety-critical applications. Prior research has focused on adaptation of algorithm based fault tolerance schemes for error detection in the dot product (linear) computations of DNNs. In this research, we show that compact machine-learned algorithmic checks inspired by prior work on linear checksums but adapted to the overall nonlinear nature of DNN computations can be used to detect both soft errors and image-triggered trojans in real-time. Experiments indicate that the method incurs low computation overhead (4%-20%), while achieving high coverage (up to 90% for soft errors and 99% for image-triggered attacks).