Disaggregated Deep Learning via In-Physics Computing at Radio Frequency

• URL: http://arxiv.org/abs/2504.17752v1
• Date: Thu, 24 Apr 2025 17:10:18 GMT
• Title: Disaggregated Deep Learning via In-Physics Computing at Radio Frequency
• Authors: Zhihui Gao, Sri Krishna Vadlamani, Kfir Sulimany, Dirk Englund, Tingjun Chen,
• Abstract summary: WISE is a novel computing architecture for wireless edge networks designed to overcome energy constraints in deep learning inference.<n>We demonstrate WISE 95.7% image classification accuracy with ultra-low operation power of 6.0 fJ/MAC per client, corresponding to a computation efficiency of 165.8 TOPS/W.
• Score: 1.0953436973292041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern edge devices, such as cameras, drones, and Internet-of-Things nodes, rely on deep learning to enable a wide range of intelligent applications, including object recognition, environment perception, and autonomous navigation. However, deploying deep learning models directly on the often resource-constrained edge devices demands significant memory footprints and computational power for real-time inference using traditional digital computing architectures. In this paper, we present WISE, a novel computing architecture for wireless edge networks designed to overcome energy constraints in deep learning inference. WISE achieves this goal through two key innovations: disaggregated model access via wireless broadcasting and in-physics computation of general complex-valued matrix-vector multiplications directly at radio frequency. Using a software-defined radio platform with wirelessly broadcast model weights over the air, we demonstrate that WISE achieves 95.7% image classification accuracy with ultra-low operation power of 6.0 fJ/MAC per client, corresponding to a computation efficiency of 165.8 TOPS/W. This approach enables energy-efficient deep learning inference on wirelessly connected edge devices, achieving more than two orders of magnitude improvement in efficiency compared to traditional digital computing.

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