Perception Workload Characterization and Prediction on the Edges with Memory Contention for Connected Autonomous Vehicles
Perception Workload Characterization and Prediction on the Edges with Memory Contention for Connected Autonomous Vehicles
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DOI：
10.1109/edge60047.2023.00026
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Sihai Tang;Shengze Wang;Song Fu;Qing Yang
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文献类型：
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作者：
Sihai Tang;Shengze Wang;Song Fu;Qing Yang
车辆边缘计算需要来自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。这种连接和卸载允许更快，更有效的数据处理，从而提高了联网车辆的安全性，性能和可靠性。现有的作品集中在处理器及其表征，但他们放弃了连接组件。内存资源和存储资源在边缘设备上是有限的，两者结合在一起对深度学习产生了重大影响。这一点很突出，因为基于感知的工作负载还有待深入研究。在我们的描述中，我们发现内存争用可以分为3种行为。这些行为中的每一个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计算时间增加了2849%，激活层增加了1173.34%。通过特征化，我们可以根据设备配置和工作负载需求对Edge的工作负载行为进行建模。通过这一点，内存争用及其影响的影响进行了量化。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的影响的工作，重点关注内存和DNN层。
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.