In the Edge Inference (EI) paradigm, where a Deep Neural Network (DNN) is split across the transceivers to wirelessly communicate goal-defined features in solving a computational task, the wireless medium has been commonly treated as a source of noise. In this paper, motivated by the emerging technologies of Reconfigurable Intelligent Surfaces (RISs) and Stacked Intelligent Metasurfaces (SIM) that offer programmable propagation of wireless signals, either through controllable reflections or diffractions, we optimize the RIS/SIM-enabled smart wireless environment as a means of over-the-air computing, resembling the operations of DNN layers. We propose a framework of Metasurfaces-Integrated Neural Networks (MINNs) for EI, presenting its modeling, training through a backpropagation variation for fading channels, and deployment aspects. The overall end-to-end DNN architecture is general enough to admit RIS and SIM devices, through controllable reconfiguration before each transmission or fixed configurations after training, while both channel-aware and channel-agnostic transceivers are considered. Our numerical evaluation showcases metasurfaces to be instrumental in performing image classification under link budgets that impede conventional communications or metasurface-free systems. It is demonstrated that our MINN framework can significantly simplify EI requirements, achieving near-optimal performance with $50~$dB lower testing signal-to-noise ratio compared to training, even without transceiver channel knowledge.

翻译：在边缘推理（EI）范式中，深度神经网络（DNN）被分割部署于收发器之间，通过无线通信传输任务定义的特征以完成计算任务，无线信道通常被视为噪声源。本文受到可重构智能表面（RIS）和堆叠智能超表面（SIM）等新兴技术的启发，这些技术通过可控反射或衍射实现对无线信号传播的可编程调控。我们将RIS/SIM赋能的智能无线环境优化为一种空中计算手段，其运作方式类似于DNN层。我们提出了用于EI的超表面集成神经网络（MINN）框架，阐述了其建模方法、通过适用于衰落信道的反向传播变体进行训练的过程以及部署方案。整体端到端DNN架构具有足够通用性，可兼容RIS和SIM设备——既支持每次传输前的可控重配置，也支持训练后的固定配置，同时考虑了信道感知与信道无关的收发器。数值评估表明，在链路预算限制下（该限制会阻碍传统通信或无超表面系统），超表面能有效执行图像分类任务。实验证明，即使收发器不具备信道知识，我们的MINN框架也能显著简化EI要求，在测试信噪比相比训练阶段低$50~$dB的条件下，仍能实现接近最优的性能。