论文标题
ADAS管道在下一代汽车平台上的智能编排
Intelligent Orchestration of ADAS Pipelines on Next Generation Automotive Platforms
论文作者
论文摘要
先进的驾驶员辅助系统（ADAS）是增加自主权水平的主要驱动因素之一，在这个相互关联的流动性时代推动了舒适性。但是，此类系统的性能是执行率的函数，该执行率需要在板上平台级别的支持。随着GPGPU平台进入汽车，存在一个机会，可以通过利用建筑异质性来适应ADAS任务的高执行率，并记住热可靠性和长期平台老化。我们提出了一个基于未来的，基于学习的自适应调度框架，该框架利用强化学习来发现基于方案的基于方案的决策，以适应增加的任务级吞吐量要求。
Advanced Driver-Assistance Systems (ADAS) is one of the primary drivers behind increasing levels of autonomy, driving comfort in this age of connected mobility. However, the performance of such systems is a function of execution rate which demands on-board platform-level support. With GPGPU platforms making their way into automobiles, there exists an opportunity to adaptively support high execution rates for ADAS tasks by exploiting architectural heterogeneity, keeping in mind thermal reliability and long-term platform aging. We propose a future-proof, learning-based adaptive scheduling framework that leverages Reinforcement Learning to discover suitable scenario based task-mapping decisions for accommodating increased task-level throughput requirements.
