## 5 Conclusions  
There will be an increased need for object detection as well as other machine learning-based approaches that are performed in a low-latency fashion in future application scenarios. For example, future Intelligent Transport Systems (ITS) will rely on pedestrian and car detection mechanisms to avoid loss of life and damage to property. Similarly, in connected autonomous driving, an object detection service is helpful for decision-making, such as for braking and obstacle avoidance. In the driver view, for example, object detection services can help the car to protect vulnerable road users (VRUs) such pedestrians and bicycles as we originally illustrated in Figure 1b.  
Approaches that rely on machine learning commonly require significant processing, which is not always available on device, but becomes available in the softwarized 5G and beyond cellular networks. We present an approach to implement a service that splits the traditional YOLOv2 model between an on-device client and centralized server component by performing only the initial layers' processing on the client and the remainder on the server. Comparing our approach with traditional on-client and on-server processing with varying degrees of client computational resources, we find that a 12-fold reduction of the service latency can be achieved when the client has 20% of the server's resources--a scenario we deem likely in future connected device scenarios, especially for battery-limited devices.  
The approach to split the intermediate results in systems incorporating neural network layers is not limited to object recognition tasks alone, but can be applied for all such systems. The increased embedding of AI approaches in modern networked systems provides broad opportunities to employ approaches such as ours to improve service levels and decrease their latency times. A particularly interesting future avenue here would be the reliance on partially pre-determined outcomes from prior cached results for distributed edge systems.  
Another venue currently under consideration is the combination of the service function split we showcased here together with network coding.  
Conceptualization, Z.X., P.S., and F.H.P.F.; methodology, Z.X., P.S., and F.H.P.F.; software, Z.X.; validation, Z.X. and P.S.; formal analysis, Z.X.; investigation, Z.X.; resources, F.H.P.F.; data curation, Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, Z.X., P.S., and F.H.P.F.; visualization, Z.X.; supervision, F.H.P.F.; project administration, F.H.P.F.; funding acquisition, F.H.P.F. All authors have read and agreed to the published version of the manuscript.  
Partially funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany's Excellence Strategy-EXC 2050/1--Project ID 390696704 --Cluster of Excellence \"Centre for Tactile Internet with Human-in-the-Loop\" (CeTI) of Technische Universitat Dresden.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth-Generation Cellular Networks \\ AI & Artificial Intelligence \\ CNN & Convolutional Neural Network \\ COIN & COmputing In the Network \\ CPU & Central Processing Unit \\ CV & Computer Vision \\ DL & Deep Learning \\ FPGA & Field-Programmable Gate Array \\ GPU & Graphics Processing Unit \\ IP & Internet Protocol \\ ITS & Intelligent Transport System \\ JPEG & Joint Photographic Experts Group \\ LIDAR & Light Detection and Ranging \\ MEC & Mobile Edge Cloud \\ NFV & Network Function Virtualization \\ RAM & Random Access Memory \\ ReLU & Rectified Linear Unit \\ SDN & Software-Defined Network \\ SFC & Service Function Chaining \\ UDP & User Datagram Protocol \\ VNF & Virtual Network Function \\ VRU & Vulnerable Road User \\ YOLO & You Look Only Once \\ WebP & Web Picture \\ \end{tabular}