Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is the key supporting technologies of B5G/6G, can achieve massive connections and high spectrum efficiency. Motivated by the benefits of NOMA, integrating NOMA with model split in MEC to reduce the inference latency further becomes attractive. However, the NOMA based communication during split inference has not been properly considered in previous works. Therefore, in this paper, we integrate the NOMA into split inference in MEC, and propose the effective communication and computing resource allocation algorithm to accelerate the model inference at edge. Specifically, when the mobile user has a large model inference task needed to be calculated in the NOMA-based MEC, it will take the energy consumption of both device and edge server and the inference latency into account to find the optimal model split strategy, subchannel allocation strategy (uplink and downlink), and transmission power allocation strategy (uplink and downlink). Since the minimum inference delay and energy consumption cannot be satisfied simultaneously, and the variables of subchannel allocation and model split are discrete, the gradient descent (GD) algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach (Li-GD) is proposed to reduce the complexity of GD algorithm that caused by the parameter discrete. Additionally, the properties of the proposed algorithm are also investigated, which demonstrate the effectiveness of the proposed algorithms.

翻译：将推理模型在设备、边缘服务器和云之间拆分可显著提升边缘智能性能。此外，作为B5G/6G的关键支撑技术，非正交多址接入（NOMA）能够实现海量连接和高频谱效率。受NOMA优势的启发，将其与移动边缘计算中的模型拆分相结合以进一步降低推理时延具有重要研究价值。然而，现有研究尚未充分考虑拆分推理过程中基于NOMA的通信问题。为此，本文在移动边缘计算的拆分推理中引入NOMA，提出高效的通信与计算资源分配算法以加速边缘模型推理。具体而言，当移动用户在基于NOMA的移动边缘计算中需要完成大规模模型推理任务时，该算法同时考虑设备与边缘服务器的能耗以及推理时延，以确定最优模型拆分策略、子信道分配策略（上行/下行）和传输功率分配策略（上行/下行）。由于最小推理时延与最小能耗无法同时满足，且子信道分配与模型拆分的变量均为离散型，本文采用梯度下降（GD）算法实现两者间的最优折中。进一步地，针对参数离散导致GD算法复杂度较高的问题，提出循环迭代梯度下降（Li-GD）方法降低算法复杂度。此外，本文还分析了所提算法的特性，验证了其有效性。