## 6 Conclusions  
This paper has investigated the issues of CNN task partitioning and task offloading in edge computing networks to enable the deployment of large CNN tasks on edge servers with limited hardware resources while minimizing task latency and energy consumption. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Time Interval/ms**} & \multicolumn{4}{c}{**Delay/ms**} & \multicolumn{4}{c}{**Energy Consumption/J**} \\ \cline{2-9}  & **Greedy** & **Genetic** & **Random** & **Fixed** & **Greedy** & **Genetic** & **Random** & **Fixed** \\ \hline
0.06 & 1.47 & 3.48 & 7.34 & 406,554 & 0.00204 & 0.0109 & 0.0272 & 0.0423 \\ \hline
0.08 & 0.96 & 3.46 & 7.44 & 402,913 & 0.00220 & 0.0112 & 0.0270 & 0.0422 \\ \hline
0.10 & 0.59 & 3.50 & 7.46 & 302,445 & 0.00187 & 0.0113 & 0.0271 & 0.0424 \\ \hline
0.12 & 0.77 & 3.46 & 7.41 & 215,295 & 0.00191 & 0.0112 & 0.0271 & 0.0402 \\ \hline
0.14 & 0.43 & 3.51 & 7.43 & 340,176 & 0.00188 & 0.0112 & 0.0271 & 0.0441 \\ \hline \end{tabular}
\end{table} 
Table 3: The influence of the request arrival rate. Figure 7: The influence of the request arrival rate. Our approach has comprised two main components: task partitioning and offloading. Instead of the conventional vertical partitioning method, we have opted for horizontal partitioning during partitioning, leveraging the characteristic that convolutional results are only relevant to part of the input data. This approach has divided the CNN task into multiple subtasks that can be executed in parallel. In modeling task offloading, we have accounted for the random arrival of tasks and employed a greedy algorithm to dynamically allocate servers in real-time, aiming to minimize task latency and energy consumption. Finally, we have designed simulation experiments to compare our algorithm with two baseline algorithms, and we have also compared scenarios with fixed partitioning granularity. The experimental results indicate that our method consistently identifies the optimal partitioning granularity for each task, achieving optimal performance when considering both latency and energy consumption. Lastly, it should be noted that our partitioning method is not designed for a specific CNN architecture but is applicable to all neural networks with a convolutional neural network layer structure, such as RNN and Transformer. This is because our resolution method determines the corresponding resolution scale according to the size of the characteristic graph output of the inference task and the hardware conditions of the device, while considering the coupling of the resolution and offloading process, and then determines the specific partition granularity based on the comprehensive index of delay and energy consumption, which is suitable for most existing CNN models. Furthermore, our partitioning method is designed based on the data locality of convolution computation. The result of the multi-layer convolution operation in CNN is only related to a specific region of the input feature graph data, and the size of this specific region can be calculated by the calculation formula given by us. For convolutional neural network architectures with complex structures and large numbers, our method is also applicable, just the granularity of the partition needs to be designed to be smaller, or large CNN networks can be divided into vertical hierarchies first, and then partitioned in parallel by our method. Conceptualization, Y.Y. and K.P. ; methodology, Z.Z. ; software, Y.Y. ; validation, Z.W., Y.Y. and Y.X. ; formal analysis, B.L. ; investigation, Y.X. ; resources, K.L. ; data curation, C.Y. ; writing--original draft preparation, Z.Z. ; writing--review and editing, Z.W. ; visualization, B.X. ; supervision, K.P. ; project administration, Z.Z. ; funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript.