# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 3 Satellite IoT Edge Intelligent Computing Architecture
### Distributed Intelligent Computing Architecture in Satellite IoT Edge Computing
#### 3.2.1 Cross-Layer Satellite IoT Edge Intelligent Computing Architecture  
At present, the scale of problems in machine learning and deep learning is getting larger and larger, and the amount of data and the number of parameters involved are also rising sharply. Traditional single-machine computing has difficulty providing enough computing power and storage resources. Therefore, distributed model training and inference are gradually becoming mainstream. Unlike single-machine training, distributed machine learning involves more details that need to be carefully considered, including global sharing of parameters and the impact of single-node performance \"short boards\" on cluster performance. In addition, in practical industrial applications, the scale of parameters generated by the data of the order of 1 TB to 1 PB during the training process is between \(10^{9}\) and \(10^{12}\)[21]. Parameter sharing has become a difficult point.  
Using the idea of distributed deep-learning training, in the satellite IoT edge intelligent computing architecture, the satellite IoT edge node near the data side can be used as the worker node to perform specific computing tasks. The advantage of this is that, in satellite-like remote-sensing observations and the like, the satellite IoT edge node is both a worker node and a data source. This way we can easily get a natural distributed data set. Therefore, each satellite IoT edge node in the distributed cluster can directly use the data collected by them for training, which saves the network bandwidth consumed by data acquisition. At the same time, satellite IoT cloud nodes that are not directly connected to the data source but have larger storage resources and higher computing power are the nodes responsible for storing and managing parameters. At the same time, satellite IoT cloud nodes with larger storage resources and more computing power are used as nodes responsible for storage and management parameters. Two types of nodes use inter-satellite links for communication and data transmission.  
In addition, considering the constraints of computing power and the constraints of power consumption of embedded devices on satellites, the neural network model must be subdivided or simplified. A deep neural network can be thought of as a directed graph with multiple network layers under each image. If the entire neural network model is run directly on the edge computing device, performance may be poor. We can design an appropriate lightweight neural network by weighing the relationship between inferential accuracy and latency. At the same time, in the satellite IoT scenario, we can store the corresponding pretraining model on the satellite IoT cloud node to accelerate the process of model training. In this process, there are two ways to obtain the pretraining model: firstly, it can be uploaded by the ground station and stored persistently in the satellite IoT cloud node; secondly, the satellite IoT cloud node can be fully utilized to obtain the pretraining model. The neural network training process of the cross-layer satellite IoT edge intelligent computing architecture is depicted in Figure 2.  
After the computing task is offloaded to the satellite cluster, the satellite IoT edge node first obtains the pretraining parameters of the model from the satellite IoT cloud nodes, and then uses the data collected by itself to optimize the model, calculate the parameter gradient, and send it to the satellite IoT cloud nodes. After that, the satellite IoT cloud nodes aggregate the gradients, and the parameters are updated and broadcast to each worker nodes. In addition to performing training tasks, inference tasks can also be performed in a space-based decentralized computing architecture. In addition to performing training tasks, inference tasks can also be performed in the satellite IoT edge intelligent computing architecture. Each satellite IoT edge node contains the latest model, which can directly infer the collected data and feed the result back to the ground data center or client.