## 3 System Model
### System Architecture  
In order to implement multi-satellite collaborative inference, we adopt a simple collaborative inference system architecture consisting of a controller and several worker nodes [23].  
As shown in Figure 2, the controller plays a crucial role in the collaborative inference system. It is a software runtime that can be deployed on a specific satellite, which is responsible for coordinating and managing the system's operations. It has a global view and can monitor the status and workload of each worker node, assigning tasks and allocating resources accordingly. The controller collects information, such as CPU and memory occupation from each worker node and based on this information, it intelligently schedules and distributes tasks to achieve faster inference and load balance.  
The worker node is the LEO satellite-based edge-computing platform that carries out specific computing tasks, e.g., submodel inference. Each worker node has CPU and memory capacity. They can communicate and collaborate with each other to jointly accomplish complex DL inference tasks. The worker nodes are connected through high-speed satellite ISLs and switches, enabling intermediate result data transfer and DNN model parameter sharing. Such a distributed computing architecture can fully leverage the resources of multiple LEO satellites, improving overall computational efficiency and inference speed.  
Before inference tasks begin, the controller intelligently splits the model into submodels suitable for different worker nodes based on their resource occupation and the submodel inference task requirements. These submodels are then assigned to the corresponding worker nodes for inference as shown in Figure 3. By well-designed model  
Figure 2: Collaborative inference system architecture.  
partitioning and task assigning, we can balance the load of each worker node and enhance the overall performance and efficiency of the system.  
Our model's operational process can be summarized in the following steps:  
1. Traverse the directed acyclic graph (DAG) structure of the NN model to find all possible splitting points. These points usually represent branches or parallel computation nodes in the NN model that can be independently computed;
2. Based on the results from the found possible splitting point, perform the finest-grained splitting of the model and save the divided parts. This divides the model into multiple smallest parts, each of which can be independently computed on different computing nodes;
3. The controller collects information about the current CPU and memory occupation of each worker node. This information helps evaluate the availability and workload of each worker node;
4. Based on the collected information, use our algorithm to calculate the optimal NN model-splitting method. This algorithm takes into account factors such as resource occupation, load balancing and transmission delay to determine the most efficient way to split the NN model;
5. Based on the NN model-splitting result from the above step, perform the model splitting and assign submodels to worker nodes sequentially, in which each computing node is responsible for processing its assigned part of the model;
6. Send the submodels to the respective worker nodes and start the computation threads. Each worker node can independently carry out its own computation tasks using the original data or intermediate inference result output by another worker node;
7. The central controller sends data to the worker node that runs the inference of the first submodel. Intermediate/final result data flows between worker nodes, and each node performs computations in the specified order, thereby accomplishing the NN model's inference task in a pipelined fashion.  
Through these steps, our collaborative inference system achieves efficient distributed DL inference tasks. By dividing the model and utilizing parallel and pipelined computations, we fully leverage the resources of multiple satellites, thereby improving the speed of model inference.