SAGE：一种用于高效端到端自动驾驶控制的分割架构方法
SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control
AI总结：
SAGE方法通过选择性云端卸载和Head Network Distillation瓶颈设计，在保证实时性的同时显著降低自动驾驶边缘设备能耗与上传数据量。
摘要：
自动驾驶汽车（AV）有望彻底改变交通运输并显著提升道路安全。然而，这些益处并非没有代价；AV需要大型深度学习（DL）模型和强大的硬件平台才能实时可靠运行，所需功耗从几百瓦到一千瓦不等。这种功耗会显著缩短车辆的行驶里程并影响排放。为解决这一问题，我们提出SAGE：一种将DL架构中关键耗能模块选择性卸载到云端的方法，以在满足实时延迟约束的同时优化边缘能耗。此外，我们利用Head Network Distillation（HND）在DL架构中引入高效瓶颈，从而在几乎不降低模型性能的情况下最小化卸载带来的网络开销成本。我们使用Nvidia Jetson TX2和业界标准的Nvidia Drive PX2作为AV边缘设备对SAGE进行评估，并证明我们的卸载策略在3G、4G LTE和WiFi技术下对广泛的DL模型和互联网连接带宽具有实用性。与仅边缘计算相比，对于配备一个低分辨率摄像头、一个高分辨率摄像头和三个高分辨率摄像头的AV，SAGE分别平均降低能耗36.13%、47.07%和55.66%。与直接摄像头卸载相比，SAGE还将上传数据量最多减少98.40%。
英文摘要：
Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety significantly. However, these benefits do not come without cost; AVs require large Deep-Learning (DL) models and powerful hardware platforms to operate reliably in real-time, requiring between several hundred watts to one kilowatt of power. This power consumption can dramatically reduce vehicles' driving range and affect emissions. To address this problem, we propose SAGE: a methodology for selectively offloading the key energy-consuming modules of DL architectures to the cloud to optimize edge energy usage while meeting real-time latency constraints. Furthermore, we leverage Head Network Distillation (HND) to introduce efficient bottlenecks within the DL architecture in order to minimize the network overhead costs of offloading with almost no degradation in the model's performance. We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge devices and demonstrate that our offloading strategy is practical for a wide range of DL models and internet connection bandwidths on 3G, 4G LTE, and WiFi technologies. Compared to edge-only computation, SAGE reduces energy consumption by an average of 36.13%, 47.07%, and 55.66% for an AV with one low-resolution camera, one high-resolution camera, and three high-resolution cameras, respectively. SAGE also reduces upload data size by up to 98.40% compared to direct camera offloading.
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