2026-01-09 デューク大学
＜関連情報＞
- https://pratt.duke.edu/news/using-the-physics-of-radio-waves-to-empower-smarter-edge-devices/
- https://www.science.org/doi/10.1126/sciadv.adz0817
無線周波数での物理計算による分散型機械学習 Disaggregated machine learning via in-physics computing at radio frequency
Zhihui Gao, Sri Krishna Vadlamani, Kfir Sulimany , Dirk Englund, and Tingjun Chen
Science Advances Published:9 Jan 2026
DOI:https://doi.org/10.1126/sciadv.adz0817
Abstract
Modern edge devices, such as cameras, drones, and internet-of-things nodes, rely on machine learning to enable a wide range of intelligent applications. However, deploying machine learning models directly on the often resource-constrained edge devices demands substantial memory footprints and computational power for real-time inference using traditional digital computing architectures. In this paper, we present WISE, computing architecture for wireless edge networks with two key innovations: disaggregated model access via over-the-air wireless broadcasting for simultaneous inference on multiple edge devices, and in-physics computation of general complex-valued matrix-vector multiplications directly at radio frequency driven by a single frequency mixer. Using a software-defined radio platform, WISE achieves 95.7% image classification accuracy (97.2% audio classification accuracy) with ultralow energy consumption of 6.0 fJ/MAC (2.8 fJ/MAC), which is more than 10× improvement compared to traditional digital computing, e.g., on modern GPUs.
