Perception Workload Characterization and Prediction on the Edges with Memory Contention for Connected Autonomous Vehicles

Sihai Tang, Shengze Wang, Song Fu, Qing Yang
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Abstract

Vehicular Edge computing requires computational power from connected Edge devices in the network to process incoming vehicle work requests. This connection and offloading allows for faster and more efficient data processing and thus improves the safety, performance, and reliability of the connected vehicles. Existing works focus on the processor and its characterization, but they forgo the connecting components. Memory resource and storage resource is limited on Edge devices, and the two combined incur a heavy impact on deep learning. This is prominent as perception-based workloads have yet to be studied deeply. In our characterization, we have found that memory contention can be split into 3 behaviors. Each of these behaviors interacts with the other resources differently. Then, in our deep neural network (DNN) layer analysis, we find several layers that see computation time increases of over 2849% for convolutional layers and 1173.34% for activation layers. Through the characterization, we can model the workload behavior for the Edge based on the device configuration and the workload requirements. Through this, the impacts of memory contention and its impacts are quantified. To the best of our knowledge, this is the first such work that characterizes the memory impacts towards vehicular edge computational workloads with a deep focus on memory and DNN layers.
网联自动驾驶汽车内存争用边缘感知工作量表征与预测
车辆边缘计算需要网络中连接的边缘设备的计算能力来处理传入的车辆工作请求。这种连接和卸载可以实现更快、更高效的数据处理,从而提高联网车辆的安全性、性能和可靠性。现有的工作集中在处理器及其特性上,但他们放弃了连接组件。内存资源和存储资源在Edge设备上是有限的,两者结合会对深度学习产生严重影响。这一点很突出,因为基于感知的工作量尚未得到深入研究。在我们的描述中,我们发现内存争用可以分为三种行为。每一种行为都以不同的方式与其他资源交互。然后,在我们的深度神经网络(DNN)层分析中,我们发现几个层的卷积层的计算时间增加了2849%以上,激活层的计算时间增加了1173.34%。通过表征,我们可以基于设备配置和工作负载需求对Edge的工作负载行为进行建模。通过这种方法,对内存争用的影响及其影响进行了量化。据我们所知,这是第一个描述内存对车辆边缘计算工作负载影响的研究,重点关注内存和深度神经网络层。
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