# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 4 Results and Discussions
### Satellite IoT Edge Intelligent Computing Architecture Performance  
We built a simulation environment in _Cloudsim4.0_[36; 37] (cloud computing simulation platform) to evaluate the training and reasoning performance of the satellite edge intelligent computing architecture. The deep-learning training and inference tasks are abstracted into different floating-point operations, and we estimate the total amount of calculation required for training based on the literature, as shown in Table 3.  
We assume that the inter-satellite link bandwidth is 300 Mbps, and the satellite-terrestrial link bandwidth is 600 Mbps. At the same time, the resource situation of each satellite can be characterized as the parameters of Table 4.  
The simulation includes processing, queuing, transmission, and propagation delay, and the propagation speed is the speed of light. Therefore, the end-to-end delay can be expressed as the following:  
\[D_{Process}=D_{P}+D_{T}+D_{L}+D_{Q}\]  
Among them, \(D_{P}\) is the processing delay of the task, \(D_{T}\) is the propagation delay, \(D_{L}\) is the data transmission delay and \(D_{Q}\) stands for queuing delay. \begin{table}
\begin{tabular}{c c} \hline \hline
**Floating point computation** & 5 TFLOPS (FP16) \\ \hline
**Operating system / Architecture** & Linux/X64 \\ \hline
**Virtual machine monitor** & XEN \\ \hline
**RAM** & 16 GB 256 bit LPDDR4x \\ \hline
**RAM Bandwidth** & 2133 MHz - 137 GB/s \\ \hline
**Disk** & 10T \\ \hline
**Power** & 30W \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The resource situation of each satellite. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Input Size** & \begin{tabular}{c} **Training Set** \\ **Size** \\ \end{tabular} & **Epochs** & \begin{tabular}{c} **Flops (One** \\ **Pass)** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Parameters** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Calculation** \\ \end{tabular} \\ \hline VGG-16 [38] & 224\({}^{\prime}\)224\({}^{\prime}\)3 & 7000 pcs & 50 & 15.470 GFLOPS & 138.38M & 5.164 PFLOPS \\ \hline ResNet-50 [39] & 224\({}^{\prime}\)224\({}^{\prime}\)3 & 7000 pcs & 50 & 3.870 GFLOPS & 25.609M & 1.292 PFLOPS \\ \hline WRN (wide & & & & & \\ residual & 224\({}^{\prime}\)224\({}^{\prime}\)3 & 7000 pcs & 50 & 10.935 GFLOPS & 68.950M & 3.650 PFLOPS \\ network) [40] & & & & & \\ \hline MobileNet [41] & 224\({}^{\prime}\)224\({}^{\prime}\)3 & 7000 pcs & 50 & 0.573 GFLOPS & 4.253M & 0.191 PFLOPS \\ \hline ShuffleNet [42] & 224\({}^{\prime}\)224\({}^{\prime}\)3 & 7000 pcs & 50 & 0.136 GFLOPS & 1.74M & 0.045 PFLOPS \\ \hline DenseNet [43] & 224\({}^{\prime}\)224\({}^{\prime}\)3 & 7000 pcs & 50 & 2.834 GFLOPS & 7.894M & 0.946 PFLOPS \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different model training and inference parameters. During the training process, the input data is the image of 224*224*3 in Table 3. The number of images trained is 7000, and the number of epochs is 50. The output data is a trained parameter, and the size of the data is the size (bytes) of the value of \"Number of parameters\". Among them, centralized training is expressed as single-node training. The process of distributed training can be expressed as the dispersion of training tasks from one satellite node to other satellite nodes. The transmission and synchronization of parameter data is involved in the distributed training process. During the training process, we will simulate the training duration of different training models on a single node and on multiple nodes, as shown in Figure 8.  
It can be seen from Figure 6 that the environment of the satellite IoT distributed network can effectively reduce the training time of the neural network with relatively large computational demand. For neural networks such as MobileNet and ShuffleNet, which have relatively small computational demands, the rate of change in training time is not obvious, but the overall training time is maintained in a relatively low range.