# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 3 Measuring and Characterizing Perception Workloads on the Edge
### Profiling Testbed and Setup  
To rigorously evaluate the memory contention effects and layer-wise performance, we designed a test environment that closely resembles real vehicular edge scenarios. Our experiments were conducted on two types of edge hardware configurations: (1) a high-end laptop simulating a powerful edge node (Intel Core i7-10750H, Nvidia GeForce RTX 2070, 16 GB DDR4 RAM, 1 TB NVMe SSD), and (2) a Nvidia Jetson Xavier NX as a representative low-end or mid-range edge device. We selected these two platforms to capture a broader spectrum of resource capabilities commonly found in vehicular edge deployments.  
Dataset and Workload Parameters: For the object detection tasks, we used inputs derived from standard AV-oriented image sets (e.g., subsets of KITTI or COCO) [54; 55]. The input resolutions varied (e.g., 320 \(\times\) 320, 640 \(\times\) 480, and 1280 \(\times\) 720) to emulate camera feeds from different AV sensors. Each resolution set was run multiple times (10 runs per setting) to capture the performance variability and to mitigate any transient system effects (such as GPU thermal throttling or background OS processes). The size of each workload was selected to reflect realistic AV perception tasks, where real-time performance is vital. For each test run, we tracked not only the inference latency but also the peak and average memory consumption, using nvidia-smi, PyTorch's built-in profiler, and Linux-based resource monitoring tools. However, it should be noted that the scope is on system-level profiling rather than perception accuracy metrics. Our approach is agnostic to the dataset, and thus we focus on memory usage and layer execution times behavior, which remain consistent across different datasets.  
Simulation Topology: To approximate a distributed edge environment, we modeled each device as if it were an individual edge node receiving inference tasks from one or more autonomous vehicles. Each node could be allocated a certain fraction of tasks, simulating real traffic scenarios where multiple vehicles offload to the same edge. The exact number of concurrent requests varied to provoke different degrees of resource contention. In more constrained tests, we artificially reduced the available RAM or GPU memory to simulate heavy oversubscription.  
Reliability and Validity Measures. We repeated each configuration (device \(\times\) resolution \(\times\) concurrency level) multiple times to ensure stable averages. We computed standard deviations and confidence intervals (95% CI) to check for statistical consistency. In addition, we systematically logged CPU frequency, GPU utilization, and GPU temperature to verify that the results were not skewed by thermal throttling or one-off system states. Where possible, we cross-checked the measured layer-by-layer timings with independent profiling scripts to validate the internal PyTorch logs. Although our dataset was substantial enough to capture meaningful trends in resource usage, we acknowledge that a larger or more diverse dataset (e.g., a full AV sensor suite) could yield additional insights. This limitation is further discussed in Sections 5 and 6.  
Figure 1: Interaction between autonomous vehicles and vehicular edge computing nodes.