The limited energy and computing resources of unmanned aerial vehicles (UAVs) hinder the application of aerial artificial intelligence. The utilization of split inference in UAVs garners significant attention due to its effectiveness in mitigating computing and energy requirements. However, achieving energy-efficient split inference in UAVs remains complex considering of various crucial parameters such as energy level and delay constraints, especially involving multiple tasks. In this paper, we present a two-timescale approach for energy minimization in split inference, where discrete and continuous variables are segregated into two timescales to reduce the size of action space and computational complexity. This segregation enables the utilization of tiny reinforcement learning (TRL) for selecting discrete transmission modes for sequential tasks. Moreover, optimization programming (OP) is embedded between TRL's output and reward function to optimize the continuous transmit power. Specifically, we replace the optimization of transmit power with that of transmission time to decrease the computational complexity of OP since we reveal that energy consumption monotonically decreases with increasing transmission time. The replacement significantly reduces the feasible region and enables a fast solution according to the closed-form expression for optimal transmit power. Simulation results show that the proposed algorithm can achieve a higher probability of successful task completion with lower energy consumption.

翻译：无人机有限的能量和计算资源制约了空中人工智能的应用。利用分割推理可有效缓解计算与能量需求，因此受到广泛关注。然而，考虑到能量水平、时延约束等关键参数，尤其是涉及多任务场景时，实现无人机节能分割推理仍具有挑战性。本文提出一种双时间尺度方法，通过将离散变量和连续变量分离至两个时间尺度，降低动作空间规模和计算复杂度，从而实现分割推理中的能量最小化。该分离策略支持采用微型强化学习为序列任务选择离散传输模式。此外，在微型强化学习输出与奖励函数之间嵌入优化规划，以优化连续传输功率。具体而言，考虑到能耗随传输时间增加而单调递减的特性，我们将传输功率优化替换为传输时间优化，从而降低优化规划的计算复杂度。该替换显著缩小了可行域，并可依据最优传输功率的闭式表达式实现快速求解。仿真结果表明，所提算法能以更低能耗实现更高的任务成功完成概率。