System Level Hardening by Computing with Matrices

Continuous advances in transistor manufacturing have enabled technology scaling along the years, sustaining Moore's law. As transistors sizes rapidly shrink, and voltage scales, the amount of charge in a node also rapidly decreases. A particle hitting the core will probably cause a transient fault to spam over several clock cycles. In this scenario, embedded systems using state-of-the-art technologies will face the challenge of operating in an environment susceptible to multiple errors, but with restricted resources available to deploy fault-tolerance, as these techniques severely increase power consumption. One possible solution to this problem is the adoption of software based fault-tolerance at the system level, aiming at reduced energy levels to ensure reliability and low energy dissipation. In this paper, we claim the detection and correction of errors on generic data structures at system level by using matrices to encode any program and algorithm. With such encoding, it is possible to employ established techniques of detection and correction of errors occurring in matrices, running with inexpressive overhead of power and energy. We evaluated this proposal using two case studies significant for the embedded system domain. Using the proposed approach, we observed in some cases an overhead of only 5% in performance and 8% in program size.