Edge-device co-inference, which concerns the cooperation between edge devices and an edge server for completing inference tasks over wireless networks, has been a promising technique for enabling various kinds of intelligent services at the network edge, e.g., auto-driving. In this paradigm, the concerned design objective of the network shifts from the traditional communication throughput to the effective and efficient execution of the inference task underpinned by the network, measured by, e.g., the inference accuracy and latency. In this paper, a task-oriented over-the-air computation scheme is proposed for a multidevice artificial intelligence system. Particularly, a novel tractable inference accuracy metric is proposed for classification tasks, which is called minimum pair-wise discriminant gain. Unlike prior work measuring the average of all class pairs in feature space, it measures the minimum distance of all class pairs. By maximizing the minimum pair-wise discriminant gain instead of its average counterpart, any pair of classes can be better separated in the feature space, and thus leading to a balanced and improved inference accuracy for all classes. Besides, this paper jointly optimizes the minimum discriminant gain of all feature elements instead of separately maximizing that of each element in the existing designs. As a result, the transmit power can be adaptively allocated to the feature elements according to their different contributions to the inference accuracy, opening an extra degree of freedom to improve inference performance. Extensive experiments are conducted using a concrete use case of human motion recognition to verify the superiority of the proposed design over the benchmarking scheme.

翻译：边缘设备协同推理涉及边缘设备与边缘服务器在无线网络上合作完成推理任务，已成为在网络边缘实现各类智能服务（例如自动驾驶）的一项前景广阔的技术。在此范式下，网络的设计目标从传统的通信吞吐量转向了由网络支撑的推理任务的有效且高效执行，其衡量指标包括推理精度和延迟等。本文针对多设备人工智能系统提出了一种以任务为导向的空中计算方案。具体而言，为分类任务提出了一种新颖且易于处理的推理精度度量指标，称为最小成对判别增益。与先前工作中测量特征空间中所有类别对平均距离的方法不同，该指标测量所有类别对的最小距离。通过最大化最小成对判别增益而非其平均对应项，特征空间中的任意类别对都能得到更好的分离，从而为所有类别带来更均衡且提升的推理精度。此外，本文联合优化了所有特征元素的最小判别增益，而非如现有设计中那样分别最大化每个元素的判别增益。因此，发射功率可以根据各特征元素对推理精度的不同贡献进行自适应分配，这为提高推理性能开辟了额外的自由度。通过人体运动识别的具体用例进行了大量实验，验证了所提设计相较于基准方案的优越性。