Exploring Deep Learning for In-Field Fault Detection in Microprocessors
Simone Duttoa, Alessandro Savino and Stefano Di Carlo
Politecnico di Torino, Control and Computer Engineering Department, Torino, Italy
astefano.dicarlo@polito.it
ABSTRACT
Nowadays, due to technology enhancement, faults are increasingly compromising all kinds of computing machines, from servers to embedded systems. Recent advances in machine 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.
Keywords: Fault Detection, Deep Learning, Monitoring Tool, Hardware Metrics.
