EARL: An Efficient Approximate HaRdware Framework for AcceLerating Fault Tree Analysis

This paper proposes an efficient hardware framework that utilizes approximate computing units to mitigate the fault tree (FT) simulation time while considering accuracy and energy. To do so, first, we introduce two hybrid-precision computational units, which can operate in either accurate or approximate modes. Then, we utilize these computational units and an accuracy propagation technique to evaluate the desired accuracy level of each computational part. Finally, EARL, the proposed framework, by taking into account determined accuracy levels, in an offline stage through machine learning technique, predicts the best hardware platform (i.e., CPU, FPGA, or GPU) for executing each benchmark and provides a trade-off among simulation time, accuracy, and energy consumption. To demonstrate EARL's efficiency, we study the case of reliability calculation through fault tree analysis. However, EARL is useful in top-down structured computational problems. Our evaluations show that the proposed framework improves the simulation time and energy consumption of fault tree analysis on average by 78.1%, and 72.6%, respectively, with a negligible accuracy loss.