With the proliferation of edge computing, efficient AI inference on edge
devices has become essential for intelligent applications such as autonomous
vehicles and VR/AR. In this context, we address the problem of efficient remote
object recognition by optimizing feature transmission between mobile devices
and edge servers. We propose an online optimization framework to address the
challenge of dynamic channel conditions and device mobility in an end-to-end
communication system. Our approach builds upon existing methods by leveraging a
semantic knowledge base to drive multi-level feature transmission, accounting
for temporal factors and dynamic elements throughout the transmission process.
To solve the online optimization problem, we design a novel soft
actor-critic-based deep reinforcement learning system with a carefully designed
reward function for real-time decision-making, overcoming the optimization
difficulty of the NP-hard problem and achieving the minimization of semantic
loss while respecting latency constraints. Numerical results showcase the
superiority of our approach compared to traditional greedy methods under
various system setups.

边缘计算下的 AI 推理在边缘设备上高效进行已成为智能应用（如自动驾驶和虚拟现实 / 增强现实）所必需的。我们针对移动设备和边缘服务器之间特征传输的优化问题，提出了一种在线优化框架。通过利用语义知识库来驱动多级特征传输，考虑传输过程中的时间因素和动态元素，我们的方法基于现有方法构建，解决了动态信道条件和设备移动的挑战。为了解决在线优化问题，我们设计了一种基于软演员 - 评论家的深度强化学习系统，并设计了一个精心设计的奖励函数来进行实时决策，克服了 NP-hard 问题的优化困难，并在尊重延迟限制的同时实现了语义损失的最小化。数值结果显示，相较于各种系统设置下的传统贪婪方法，我们的方法具有优势。

学习动态通道中的基于语义知识库指导的在线特征传输

Learning for Semantic Knowledge Base-Guided Online Feature Transmission  in Dynamic Channels

Soft sensors are crucial in bridging autonomous systems' physical and digital
realms, enhancing sensor fusion and perception. Instead of deploying soft
sensors on the Cloud, this study shift towards employing on-device soft
sensors, promising heightened efficiency and bolstering data security. Our
approach substantially improves energy efficiency by deploying Artificial
Intelligence (AI) directly on devices within a wireless sensor network.
Furthermore, the synergistic integration of the Microcontroller Unit and
Field-Programmable Gate Array (FPGA) leverages the rapid AI inference
capabilities of the latter. Empirical evidence from our real-world use case
demonstrates that FPGA-based soft sensors achieve inference times ranging
remarkably from 1.04 to 12.04 microseconds. These compelling results highlight
the considerable potential of our innovative approach for executing real-time
inference tasks efficiently, thereby presenting a feasible alternative that
effectively addresses the latency challenges intrinsic to Cloud-based
deployments.

本研究通过在设备上部署软传感器，提高传感器融合和感知能力，改善能源效率，并利用可编程逻辑门阵列（FPGA）和微控制器单元的协同整合，提供了一种具有潜力的创新方法来高效地执行实时推理任务。