The upcoming sixth Generation (6G) of wireless networks envisions ultra-low latency and energy efficient Edge Inference (EI) for diverse Internet of Things (IoT) applications. However, traditional digital hardware for machine learning is power intensive, motivating the need for alternative computation paradigms. Over-The-Air (OTA) computation is regarded as an emerging transformative approach assigning the wireless channel to actively perform computational tasks. This article introduces the concept of Metasurfaces-Integrated Neural Networks (MINNs), a physical-layer-enabled deep learning framework that leverages programmable multi-layer metasurface structures and Multiple-Input Multiple-Output (MIMO) channels to realize computational layers in the wave propagation domain. The MINN system is conceptualized as three modules: Encoder, Channel (uncontrollable propagation features and metasurfaces), and Decoder. The first and last modules, realized respectively at the multi-antenna transmitter and receiver, consist of conventional digital or purposely designed analog Deep Neural Network (DNN) layers, and the metasurfaces responses of the Channel module are optimized alongside all modules as trainable weights. This architecture enables computation offloading into the end-to-end physical layer, flexibly among its constituent modules, achieving performance comparable to fully digital DNNs while significantly reducing power consumption. The training of the MINN framework, two representative variations, and performance results for indicative applications are presented, highlighting the potential of MINNs as a lightweight and sustainable solution for future EI-enabled wireless systems. The article is concluded with a list of open challenges and promising research directions.

翻译：即将到来的第六代（6G）无线网络旨在为多样化的物联网应用提供超低延迟与高能效的边缘推理能力。然而，传统的机器学习数字硬件能耗较高，这推动了对替代性计算范式的需求。空中计算被视为一种新兴的变革性方法，它利用无线信道主动执行计算任务。本文提出了超表面集成神经网络的概念——一种物理层实现的深度学习框架，通过可编程多层超表面结构与多输入多输出信道，在波传播域中实现计算层。MINN系统被概念化为三个模块：编码器、信道（包含不可控传播特性与超表面）和解码器。首尾模块分别由多天线发射端和接收端实现，包含传统数字层或专门设计的模拟深度神经网络层；信道模块的超表面响应则与所有模块共同作为可训练权重进行优化。该架构支持将计算任务卸载至端到端物理层，并可在其组成模块间灵活分配，在实现与全数字深度神经网络相当性能的同时，显著降低功耗。本文阐述了MINN框架的训练方法、两种典型变体结构以及在典型应用中的性能表现，彰显了MINN作为未来支持边缘推理的无线系统之轻量化可持续解决方案的潜力。文章最后总结了当前面临的开放挑战与前景广阔的研究方向。