AirNN: Over-the-Air Computation for Neural Networks via Reconfigurable Intelligent Surfaces

• Published in: IEEE/ACM transactions on networking Vol. 31; no. 6; pp. 1 - 0
• Main Authors: Sanchez, Sara Garcia, Reus-Muns, Guillem, Bocanegra, Carlos, Li, Yanyu, Muncuk, Ufuk, Naderi, Yousof, Wang, Yanzhi, Ioannidis, Stratis, Chowdhury, Kaushik Roy
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: analog convolution; Artificial neural networks; Computation offloading; Computer architecture; convolutional neural network; FIR filters; Neural networks; Over-the-air computation; programmable wireless environment; reconfigurable intelligent surface; Reconfigurable intelligent surfaces; Wave reflection
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2022.3225883

Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement the first-of-its-kind convolution that uses over-the-air computation and demonstrate it for inference tasks in a convolutional neural network (CNN). We engineer the ambient wireless propagation environment through reconfigurable intelligent surfaces (RIS) to design such an architecture, which we call 'AirNN'. AirNN leverages the physics of wave reflection to represent a digital convolution, an essential part of a CNN architecture, in the analog domain. In contrast to classical communication, where the receiver must react to the channel-induced transformation, generally represented as finite impulse response (FIR) filter, AirNN proactively creates the signal reflections to emulate specific FIR filters through RIS. AirNN involves two steps: first, the weights of the neurons in the CNN are drawn from a finite set of channel impulse responses (CIR) that correspond to realizable FIR filters. Second, each CIR is engineered through RIS, and reflected signals combine at the receiver to determine the output of the convolution. This paper presents a proof-of-concept of AirNN by experimentally demonstrating convolutions with over-the-air computation. We then validate the entire resulting CNN model accuracy via simulations for an example task of modulation classification.