Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints. Typical for such systems, however, is to have their controllers designed with formal guarantees on safety that precede in priority such optimizations, which in turn limits their application in real settings. In this paper, we propose a novel energy optimization framework that is aware of the autonomous system's safety state, and leverages it to regulate the application of energy optimization methods so that the system's formal safety properties are preserved. In particular, through the formal characterization of a system's safety state as a dynamic processing deadline, the computing workloads of the underlying models can be adapted accordingly. For our experiments, we model two popular runtime energy optimization methods, offloading and gating, and simulate an autonomous driving system (ADS) use-case in the CARLA simulation environment with performance characterizations obtained from the standard Nvidia Drive PX2 ADS platform. Our results demonstrate that through a formal awareness of the perceived risks in the test case scenario, energy efficiency gains are still achieved (reaching 89.9%) while maintaining the desired safety properties.

翻译：摘要：运行时能量管理对于边缘多传感器自主系统在平台约束下实现高性能已变得至关重要。然而，此类系统的典型特征是，其控制器在设计时优先考虑形式化安全保障，且此类保障在能量优化中具有更高优先级，这反而限制了优化方法在实际场景中的应用。本文提出一种新颖的能量优化框架，该框架能够感知自主系统的安全状态，并据此调控能量优化方法的实施，从而确保系统的形式化安全属性得以维持。具体而言，通过将系统安全状态形式化表征为动态处理截止时间，底层模型的计算负载可相应调整。在实验中，我们建模了两种常见的运行时能量优化方法（卸载与门控），并在CARLA仿真环境中模拟了自主驾驶系统（ADS）用例，同时采用标准Nvidia Drive PX2 ADS平台性能特征进行分析。结果表明，通过对测试场景中感知风险的形式化感知，系统在保持期望安全属性的同时，仍可实现能量效率增益（最高达89.9%）。