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.

无人机的能量和计算资源有限制，本研究使用分割推理技术旨在减少计算和能量需求，通过两个不同时间尺度的方法来降低行动空间和计算复杂性，实现能量最小化，以提高任务完成的概率并降低能量消耗。

多任务切分推理中的高效能量控制：基于微型学习的方法 —— 无人机应用

Energy-Efficient Power Control for Multiple-Task Split Inference in  UAVs: A Tiny Learning-Based Approach

Split inference partitions a deep neural network (DNN) to run the early part
at the edge and the later part in the cloud. This meets two key requirements
for on-device machine learning: input privacy and compute efficiency. Still, an
open question in split inference is output privacy, given that the output of a
DNN is visible to the cloud. While encrypted computing can protect output
privacy, it mandates extensive computation and communication resources. In this
paper, we introduce "Salted DNNs": a novel method that lets clients control the
semantic interpretation of DNN output at inference time while maintaining
accuracy and efficiency very close to that of a standard DNN. Experimental
evaluations conducted on both image and sensor data show that Salted DNNs
achieve classification accuracy very close to standard DNNs, particularly when
the salted layer is positioned within the early part to meet the requirements
of split inference. Our method is general and can be applied to various DNNs.
We open-source our code and results, as a benchmark for future studies.

通过分割推理和 Salted DNNs 方法，满足了边缘计算中保护输入和输出隐私的要求，并在保持了类似标准 DNN 的准确性和效率的同时，为未来研究提供了一个基准。