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Reliability-Aware Platform-Based Design Methodology for Energy-Efficient Multiprocessor Systems

Lifetime reliability is an emerging concern in multiprocessor systems as escalating power density and hence temperature variation continues to accelerate wear-out leading to a growing prominence of device defects. In this paper, we propose a platform-based design methodology that involves performanceaware task mapping on a multiprocessor system to jointly minimize energy consumption and temperature related wear-out. Fundamental to this methodology is a simplified temperature model that incorporates not only the transient and steady-state behavior (temporal effect) but also its dependency on the temperature of the surrounding cores (spatial effect). The proposed temperature model is integrated in a gradient-based fast heuristic that controls the voltage and frequency of the cores to limit the average and peak temperature leading to a longer lifetime, simultaneously minimizing the energy consumption. Lifetime computation considers task remapping, which is a common feature available in modern multiprocessor systems. A linear programming approach is then proposed to distribute the cores of a multiprocessor system among concurrent applications to maximize the lifetime. Experiments conducted with a set of synthetic and real-life applications represented as synchronous data flow graphs demonstrate that the proposed approach minimizes energy consumption by an average 24% with 47% increase in lifetime. For concurrent applications, the proposed lifetime-aware core distribution results in an average 10% improvement in lifetime as compared to the performance-aware core distribution.