## 6 Related Work  
In this section, we conducted a theoretical analysis of the existing research on accelerating the DNN inference of edge-distributed inference. To the best of our knowledge, there is no research on edge-distributed inference in the SEC, so we analyzed the related works in terrestrial edge computing.  
To accelerate the inference of DNN, there is a work to achieve the distributed DNN inference using pruning [44]. In [44], the authors use a class-aware pruning scheme [56] to trim the original DNN so that the new model small DNN (SNN) can only cover a portion of the original output categories. Using this principle, the author obtains several SNNs for distinguishing output categories based on the original DNN, which are deployed on multiple edge servers and collaborate to obtain inference results that can fully cover the category range of the original output. However, changing the parameters and structure of the original model can easily have unpredictable impacts on the inference accuracy.  
Figure 11: The latency iterations of VGG16 and AlexNet.  
Figure 10: The data transmission distribution and the workers’ out-bandwidth distribution.  
There is also a method that proposes a loosely coupled CNN structure to fundamentally solve the problem of distributed deployment [42]. In [42], the authors designed a new loosely coupled structure (LCS) to adapt the distributed CNN inference. However, this approach faces the need for retraining new models, requiring different parameters for different purposes and corresponding to different datasets, which is a huge consumption of computing resources and time.  
The method that distributed partition DNNs effectively avoids the challenges brought by the above two methods. Distributed partitioning and the deployment of DNNs can take place without changing the structure and parameters of the original model, and it does not need to retrain the model [38; 41; 57; 58; 59]. In [41], the authors deploy workload in a distributed manner based on network layer types. They use an input partition for convolution layers and a weight partition for full connection layers. BiasedOne-Dimensional Partition (BODP), Modified Spectral Co-Clustering (MSCC), and Modified Spectral Co-Clustering (MSCC) are proposed as adjuncts. In [38], the authors propose an Fused Tile Partitioning (FTP) method to parallelize the convolution operation; it can divide the feature maps of each layer into small tiles in a grid fashion. Both [57; 58] rely on Deep Reinforcement Learning (DRL). In [57], the authors search for an optimal partition location for each layer volume using the Layer Configuration-based Partition Scheme Search (LC-PSS). For a layer volume splitter, they model the split process as a Markov Decision Process (MDP) and use DRL to make optimal split decisions. In [58], the authors use DRL techniques to assist in task allocation in edge-distributed inference. Modeling the partition process as MDP, DRL agents use inference latency and layer configuration as the rewards and states, and then the optimal segmentation decisions for each layer volume are made one by one. In [59], the authors use a host edge server to configure multiple secondary edge servers, and the overlapping zone of sub-tasks on the secondary edge servers is executed on the host edge server.  
Considering that these distributed partitioning studies did not consider the trade-off between communication latency and computation latency, which is important for shortening inference latency and trick, we propose the EDIJP framework. Our proposed framework is based on joint partitioning for distributed deployment. We model the data partition problem as an LP problem and design an iterative algorithm to achieve the trade-off of communication latency and computation latency so that the inference latency can be shortened as much as possible.