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Demystifying Power and Performance Bottlenecks in Autonomous Driving Systems

机译：揭开自动驾驶系统中动力和性能瓶颈的神秘面纱

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Autonomous Vehicles (AVs) have the potential to radically change the automotive industry. However, computing solutions for AVs have to meet severe performance and power constraints to guarantee a safe driving experience. Current solutions either exhibit high cost and power dissipation or fail to meet the stringent latency constraints. Therefore, the popularization of AVs requires a low-cost yet effective computing system. Understanding the sources of latency and energy consumption is key in order to improve autonomous driving systems. In this paper, we present a detailed characterization of Autoware, a modern self-driving car system. We analyze the performance and power of the different components and leverage hardware counters to identify the main bottlenecks. Our approach to AV characterization avoids pitfalls of previous works: profiling individual components in isolation and neglecting LiDAR-related components. We base our characterization on a rigorous methodology that considers the entire software stack. Profiling the end-to-end system accounts for interference and contention among different components that run in parallel, also including memory transfers to communicate data. We show that all these factors have a high impact on latency and cannot be measured by profiling isolated modules. Our characterization provides novel insights, some of the interesting findings are the following. First, contention among different modules drastically impacts latency and performance predictability. Second, LiDAR-related components are important contributors to the latency of the system. Finally, a modern platform with a high-end CPU and GPU cannot achieve real-time performance when considering the entire end-to-end system.
机译：自治车辆（AVS）有可能从彻底改变汽车行业。但是，AVS的计算解决方案必须满足严重的性能和功率约束，以保证安全的驾驶体验。目前的解决方案展示了高成本和功耗，或者未能满足严格的延迟约束。因此，AVS的普及需要低成本但有效的计算系统。了解潜伏期和能源消耗来源是键，以改善自动驾驶系统。在本文中，我们提供了一种现代自动驾驶汽车系统的自动护理的详细表征。我们分析了不同组件的性能和功率，并利用硬件计数器来识别主要瓶颈。我们的AV表征方法避免了以前的作品的陷阱：分离和忽视与激光雷达相关的组件进行分析。我们基于考虑整个软件堆栈的严格方法的特征。分析端到端系统的分析帐户用于并行运行的不同组件之间的干扰和争用，还包括用于传送数据的内存传输。我们展示所有这些因素对延迟产生高影响力，不能通过分析隔离模块来衡量。我们的表征提供了新颖的见解，一些有趣的发现是以下内容。首先，不同模块之间的争用大大影响延迟和性能可预测性。其次，与系统潜伏期的延迟是重要的贡献者。最后，在考虑整个端到端系统时，具有高端CPU和GPU的现代平台无法实现实时性能。

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