Predicting Failures in Embedded Systems Using Long Short-Term Inference

Users of embedded and cyber-physical systems expect dependable operation for an increasingly diverse set of applications and environments. Reactive self-diagnosis techniques either use unnecessarily conservative guardbands, or do not prevent catastrophic failures. In this letter, we utilize machine-learning techniques to design a prediction engine in order to predict failures on-device in the embedded systems. We evaluate our prediction engine's effectiveness for predicting temperature behavior on a mobile system-on-chip and propose a realizable hardware implementation for the use-case.