CE-Based Optimization for Real-time System Availability under Learned Soft Error Rate
Liying Li1, Tongquan Wei1, Junlong Zhou2, Mingsong Chen3 and Xiaobo Sharon Hu4
1Department of Computer Science and Technology, East China Normal University
2School of Computer Science and Engineering, Nanjing University of Science and Technology
3Shanghai Key Lab of Trustworthy Computing, East China Normal University
4Department of Computer Science and Engineering, University of Notre Dame
ABSTRACT
As the density of integrated circuits continues to increase, the possibility that real-time systems suffer from transient and permanent failures rises significantly, resulting in a degraded availability of system functionality. In this paper, we investigate the dynamic modeling of transient failure rate based on Back Propagation (BP) neural network, and propose an optimization strategy for system availability based on Cross Entropy (CE). Specifically, the neural network is trained using cross-layer simulation data obtained from SPICE simulation while the CE-based optimization for system functionality availability is achieved by judiciously selecting an optimal supply voltage for processors under timing constraints. Simulation results show that the proposed method can achieve system availability improvement of up to 32% compared to benchmarking methods.
