# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 6 Conclusions and Future Works  
Machine learning and AI workloads have become stable components of our current society, driven by the widespread adoption of edge technology by major companies such as Microsoft Azure, Amazon AWS, and IBM [59; 60; 61]. However, utilizing on-road edge to offload workloads from connected and autonomous vehicles (CAVs) introduces many uncertainties. Existing research on optimizing machine learning for edge devices lacks the in-depth insights necessary for edge users and device providers to accurately predict the behavior of machine learning workloads across various configurations. In this paper, we tackle these challenges and identify areas for potential improvement. We propose a novel approach to optimizing workloads by considering edge device parameters, workloadinputs, and algorithm architectures. The profiling and insights provided in our approach can be generalized beyond the specific machine learning algorithms discussed here. While our models are fundamental, they provide a clearer characterization of workload behavior compared to traditional machine learning (ML) models. Although some models, like MLP and Correlated Nystrom Views, can predict workload behavior, they require extensive training and struggle to accommodate diverse ML inference algorithms. By characterizing and generalizing our findings, we offer valuable insights into the performance and capabilities of edge devices for machine learning workloads. Our analysis indicates that even under memory contention, algorithms such as YOLO and Faster R-CNN can successfully complete their tasks, suggesting that resource oversubscription on edge devices is a feasible option for non-time-sensitive applications. Moreover, utilizing memory and layer-wise information allows for the optimization of both the architecture and deployment of machine learning models, enhancing the efficiency of edge devices and reducing energy consumption. Our analysis identifies critical bottlenecks in machine learning workloads as detailed in Sections 4 and 5. The ability to predict the execution time of specific layers presents several advantages for future research. First, it enables the optimization of model architecture and hyperparameters by pinpointing the most time-consuming layers and enhancing their performance. Second, it improves model efficiency during training or inference by scheduling layers to maximize available resources and minimize overall execution time. Finally, it supports the development of new algorithms and techniques that consider the performance characteristics of individual layers, leading to the creation of more efficient and accurate machine learning models. The contributions of our paper are significant. Our proposed approach offers a targeted method for predicting the performance and maximum workload capabilities of edge devices for machine learning inference tasks. These insights empower other researchers and service providers to pursue future research on more efficient algorithms and resource management strategies, including memory and processing power, ultimately reducing contention and enhancing overall performance. Additionally, exploring distributed systems and edge-to-cloud architectures could help mitigate resource contention and improve scalability. In conclusion, there are numerous potential areas for future research in profiling machine learning workloads on edge devices. Addressing these challenges will be vital for facilitating the widespread deployment of machine learning models in edge environments. By investigating these areas, researchers can advance efficient algorithms, resource management strategies, profiling tools, and privacy-preserving techniques, ultimately enhancing the seamless integration of machine learning within edge computing frameworks. Conceptualization, S.T. and S.F. ; methodology, S.T. ; software, W.F. ; validation, W.F., Y.H. and D.C.; formal analysis, W.F. ; investigation, Y.H. ; resources, D.C.; data curation, S.W. ; writing--original draft preparation, S.T.