Thermal Aware System-Wide Reliability Optimization for Automotive Distributed Computing Applications

Abstract

As the automotive industry is shifting the paradigm towards autonomous driving, safety guarantee has become a paramount consideration. Temperature plays a key role in the system-wide reliability of the electronic control systems (ECS) used in the automotive. A vehicle is usually subjected to harsh temperature conditions from its operating environment. The increasing power density of IC chips in the ECS further exacerbates the operating temperature and thermal gradient condition on the chip, thereby significantly impacting the vehicle’s reliability. In this paper, we study how to map a periodic distributed automotive application on a heterogeneous multiple-core processing architecture with temperature and system-level reliability issues in check. We first present a mathematical programming model to bound the peak operating temperature for the ECS. Then we propose a more sophisticated approach based on the genetic algorithm to effectively bound the peak temperature and optimize the system-wide reliability of the ECS by maximizing its mean-time-to-failure (MTTF). To this end, we present an algorithm to guarantee the peak temperature for periodic applications with variable execution times to ensure our approach’s effectiveness. We also present several computationally efficient techniques for system-wide MTTF computation, which show several-order-of-magnitude speed-up over the state-of-the-art method when tested using synthetic cases and practical benchmarks.