In recent studies, collaborative intelligence (CI) has emerged as a promising framework for deployment of Artificial Intelligence (AI)-based services on mobile/edge devices. In CI, the AI model (a deep neural network) is split between the edge and the cloud, and intermediate features are sent from the edge sub-model to the cloud sub-model. In this article, we study bit allocation for feature coding in multi-stream CI systems. We model task distortion as a function of rate using convex surfaces similar to those found in distortion-rate theory. Using such models, we are able to provide closed-form bit allocation solutions for single-task systems and scalarized multi-task systems. Moreover, we provide analytical characterization of the full Pareto set for 2-stream $k$ -task systems, and bounds on the Pareto set for 3-stream 2-task systems. Analytical results are examined on a variety of DNN models from the literature to demonstrate wide applicability of the results.

中文翻译：

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协同智能的帕累托最优位分配

在最近的研究中，协作智能（CI）已经成为在移动/边缘设备上部署基于人工智能（AI）的服务的有前途的框架。在CI中，将AI模型（一个深度神经网络）在边缘和云之间进行拆分，并将中间特征从边缘子模型发送到云子模型。在本文中，我们研究了多流CI系统中用于特征编码的位分配。我们使用类似于畸变率理论中发现的凸面，将任务畸变建模为率的函数。使用这样的模型，我们能够为单任务系统和标量多任务系统提供封闭形式的位分配解决方案。此外，我们提供了2个流的完整Pareto集的分析特征 $ k $ -任务系统，以及3流2任务系统的Pareto范围。分析结果在文献中的各种DNN模型上进行了检验，以证明结果的广泛适用性。