As the density of integrated circuits continues to increase, the possibility that real-time systems suffer from soft and hard errors rises significantly, resulting in a degraded availability of system. In this article, we investigate the dynamic modeling of cross-layer soft error rate based on the Back Propagation (BP) neural network, and propose optimization strategies for system availability based on Cross Entropy (CE) and Q-learning algorithms. Specifically, the BP neural network is trained using cross-layer simulation data obtained from SPICE simulation while the optimization for system availability is achieved by judiciously selecting an optimal supply voltage for processors under timing constraints. Simulation results show that the CE-based method can improve system availability by up to 32 percent compared to state-of-the-art methods, and the Q-learning-based algorithm can further enhance system availability by up to 20 percent compared to the proposed CE-based method.

中文翻译：

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基于学习的实时系统可用性建模和优化

随着集成电路密度的不断提高，实时系统遭受软错误和硬错误的可能性显着增加，从而导致系统可用性下降。在本文中，我们研究了基于反向传播（BP）神经网络的跨层软错误率的动态建模，并提出了基于交叉熵（CE）和Q学习算法的系统可用性优化策略。具体而言，使用从SPICE仿真获得的跨层仿真数据训练BP神经网络，同时通过在时序约束下明智地选择处理器的最佳电源电压来实现系统可用性的优化。仿真结果表明，与最先进的方法相比，基于CE的方法最多可将系统可用性提高32％，