SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control
SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control
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DOI：
10.1145/3477006
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发表时间：
2021-07
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通讯作者：
A. Malawade;Mohanad Odema;Sebastien Lajeunesse-DeGroot;M. A. Al Faruque
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作者：
A. Malawade;Mohanad Odema;Sebastien Lajeunesse-DeGroot;M. A. Al Faruque
自动驾驶汽车（AV）有望彻底改变交通运输，并显着改善道路安全。然而，这些好处并不是没有代价的; AV需要大型深度学习（DL）模型和强大的硬件平台来实时可靠地运行，需要数百瓦到一千瓦的功率。这种电力消耗会大大减少车辆的行驶里程并影响排放。为了解决这个问题，我们提出了SAGE：一种选择性地将DL架构的关键能耗模块卸载到云端的方法，以优化边缘能源使用，同时满足实时延迟限制。此外，我们利用头网络蒸馏（HND）引入有效的DL架构内的瓶颈，以尽量减少网络开销的卸载成本，几乎没有降低模型的性能。我们使用Nvidia Jetson TX 2和行业标准的Nvidia Drive PX 2作为AV边缘设备评估SAGE，并证明我们的卸载策略适用于各种DL模型和3G，4G LTE和WiFi技术上的互联网连接带宽。与仅边缘计算相比，SAGE对于具有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分别平均降低了36.13%、47.07%和55.66%的能耗。与直接相机卸载相比，SAGE还将上传数据大小减少了98.40%。
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.