## 2 Related Work  
### Collaborative Inference  
In order to improve the inference performance, there are some works that focus on collaborative inference in the literature.  
[PERSON] et al. study edge inference in resource-constrained devices and focus on device edge co-inference, in which the inference is assisted by an edge-computing server [11]. They propose a general three-step framework for the inference: model split-point selection to determine the on-device model; communication-aware model compression to reduce the on-device computation and the resulting communication overhead; and task-oriented encoding of the intermediate feature to further reduce the communication overhead. They only consider splitting the model into two parts. However, in order to utilize many satellites' resources, we limit ourselves to only using one satellite's computing resource.  
[PERSON] et al. explore the distributed DNN deployment problem on the next-generation network, which includes multi-level hierarchical computing units from the UE to the cloud by leveraging FC and MEC [12]. The computing units in the lower level (i.e., nearer to the UE) usually have weaker computing and bandwidth capacity (e.g., 10 Gflops and 1 Gbps), whereas the ones in the higher level (i.e., nearer to the cloud) are more powerful (e.g., 1 Tflops and 10 Gbps). They solve the deployment problem considering both response time and inference throughput. They study the distributed DNN deployment problem considering multi-level hierarchical computing units, which is different from this paper.  
[PERSON] et al. introduce methods for distributed inference over IoT devices and edge servers [13]. They propose two distinct algorithms to split the deep neural network layers' computation between the IoT device and an edge server: the early split strategy (ESS) for battery-powered IoT devices and the late split strategy (LSS) for IoT devices connected to a regular power source, considering several factors, such as the available bandwidth of ISLs, the available computing resource of satellites, the computing power needed for the inference process as part of the DNN model, etc. They study the distributed inference by the IoT device and the edge server, which is different to our problem.  
[PERSON] et al. present experimental results of DNN inference offloading in a real-world 5G MEC testbed to meet requirements for high detection accuracy and low end-to-end latency for object detection [14]. They implement the DNN offloading by applying task pipeline parallelism and a DNN task-decoupling scheme from the edge to the MEC server. However, they offload the whole inference task to the MEC server, which is different from our approach, which splits the inference task into multiple parts.  
[PERSON] et al. study the cloud-based model serving problem, in which users deploy models as serverless functions and let the platform handle provisioning and scaling [15]. They observe that serverless functions have constrained resources in CPU and memory, making them inefficient or infeasible to serve the whole large neural networks. As such, they present Gillis, a serverless-based model serving system that automatically partitions a large model across multiple serverless functions for faster inference and reduced memory footprint per function. In their scenario, the DNN model partitions are deployed on different serverless functions between which are low-delay links with large bandwidth, which is different from the satellites' collaborative inference scenario in which ISLs have high delay.