Task-oriented integrated sensing, communication, and computation (ISCC) is a key technology for achieving low-latency edge inference and enabling efficient implementation of artificial intelligence (AI) in industrial cyber-physical systems (ICPS). However, the constrained energy supply at edge devices has emerged as a critical bottleneck. In this paper, we propose a novel energy-efficient ISCC framework for AI inference at resource-constrained edge devices, where adjustable split inference, model pruning, and feature quantization are jointly designed to adapt to diverse task requirements. A joint resource allocation design problem for the proposed ISCC framework is formulated to minimize the energy consumption under stringent inference accuracy and latency constraints. To address the challenge of characterizing inference accuracy, we derive an explicit approximation for it by analyzing the impact of sensing, communication, and computation processes on the inference performance. Building upon the analytical results, we propose an iterative algorithm employing alternating optimization to solve the resource allocation problem. In each subproblem, the optimal solutions are available by respectively applying a golden section search method and checking the Karush-Kuhn-Tucker (KKT) conditions, thereby ensuring the convergence to a local optimum of the original problem. Numerical results demonstrate the effectiveness of the proposed ISCC design, showing a significant reduction in energy consumption of up to 40% compared to existing methods, particularly in low-latency scenarios.

翻译：面向任务的集成感知、通信与计算（ISCC）是实现低延迟边缘推理、推动人工智能（AI）在工业信息物理系统（ICPS）中高效部署的关键技术。然而，边缘设备有限的能量供给已成为关键瓶颈。本文针对资源受限的边缘设备，提出一种新颖的能量高效ISCC框架用于AI推理，其中联合设计了可调节的分割推理、模型剪枝与特征量化，以适应多样化的任务需求。针对所提ISCC框架，我们构建了一个联合资源分配设计问题，旨在严格的推理精度与延迟约束下最小化能量消耗。为应对推理精度表征的挑战，我们通过分析感知、通信与计算过程对推理性能的影响，推导出其显式近似表达式。基于该分析结果，我们提出一种采用交替优化的迭代算法来求解资源分配问题。在每个子问题中，通过分别应用黄金分割搜索方法与检验Karush-Kuhn-Tucker（KKT）条件可获得最优解，从而确保算法收敛至原问题的局部最优解。数值结果验证了所提ISCC设计的有效性，与现有方法相比，其能量消耗最高可降低40%，尤其在低延迟场景中效果显著。