# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 1 Introduction  
Perception is crucial for autonomous vehicles (AVs). While AVs have the potential to be highly safe, they are not infallible. All automotive manufacturers prioritize safety and efficiency in their designs, yet unforeseen challenges can still arise. The foremost responsibility of an AV is to ensure the safety of its passengers, making it essential for the vehicle to make critical driving decisions based on its surrounding environment. Additionally, the AV must process data efficiently--from sensing to driving--to respond in a timely manner to both its own actions and those of nearby entities. The inherent nature of AVs presents a gap within traditional edge computing research, and we seek to address some of these challenges below. In this framework, the primary processing pipeline of an AV involves data flowing from sensors to perception, then to path planning, and finally to actuation. Among thesecomponents, the object detection module is arguably the most vital, as underscored by numerous news reports [1; 2; 3]. Object detection demands both speed and accuracy, making Deep Neural Networks (DNNs) the most fitting choice for this module. DNNs have a robust history and are actively researched, finding applications in both academic and industrial settings. Various DNN implementations, such as YOLO, SSD, Faster R-CNN, DETR, Deformable DETR and PointPillars for object detection [4; 5; 6; 7; 8; 9], DeepLab, DeepLabV3+, and ViT for image segmentation [10; 11; 12], and LaneNet for lane detection [13], demonstrate their effectiveness across tasks. These DNN workloads are typically executed as edge workloads; however, for a DNN to be effective, it must be continuously trained and optimized to address new challenges that arise over time. For example, the emergence of new climates or unmarked older roads necessitates this adaptability. Consequently, many companies employ human operators and semi-supervised methods to label and train the vast amounts of data generated by vehicles on the road, with each vehicle producing nearly \(11\) terabytes of data daily [14]. Moreover, while edge computing offers significantly lower latency compared to cloud solutions, it also comes with more limited resources. This scarcity of computing power makes understanding perception workloads on edge devices a critical aspect of vehicle and edge computing [15]. As a mode of transport, AVs must adhere to stringent safety standards, where latency and detection accuracy are paramount [16]. However, meeting real-time requirements is notoriously challenging, as reflected by the extensive research in this domain driven by continuous technological advancements [17]. Given the widespread use of DNNs in perception tasks, the pressing issue is how AVs and edge nodes can effectively manage these workloads without necessitating constant upgrades of sensors and models. Since most AVs rely on their own data for real-time object detection, they face inherent limitations, particularly regarding sensor blind zones and obstructions [18]. In this paper, we investigate memory contention not only in convolutional neural networks (CNNs) but also across individual layers, focusing on both characterization and prediction. To the best of our knowledge, this work is the first to comprehensively analyze the impact of memory contention on the performance of perception workloads in edge environments. Existing studies often highlight the CPU and GPU as limiting factors, for example, [19] systematically discusses the configuration of CPU and GPU in autonomous driving systems, neglecting the crucial role of memory. Memory usage is a significant consideration that can profoundly affect the deployment of machine learning models for inference on edge devices. These models typically require substantial memory for training and operation, and their size can escalate rapidly with increased complexity.