Exploring Deep Learning for In-Field Fault Detection in Microprocessors

Nowadays, due to technology enhancement, faults are increasingly compromising all kinds of computing machines, from servers to embedded systems. Recent advances in ma- chine learning are opening new opportunities to achieve fault detection exploiting hardware metrics inspection, thus avoiding the use of heavy software techniques or product-specific errors reporting mechanisms. This paper investigates the capability of different deep learning models trained on data collected through simulation-based fault injection to generalize over different software applications.