用无线电直接计算神经网络，让边缘设备省电百倍以上。
Disaggregated Deep Learning via In-Physics Computing at Radio Frequency
- 模型权重无线广播，客户端直接在射频端完成复杂计算。
- 图像识别准确率达95.7%，每操作仅耗6.0飞焦，能效165.8 TOPS/W。
- 适合资源受限的物联网、无人机等低功耗智能设备使用。
现代边缘设备如摄像头、无人机和物联网节点依赖深度学习实现目标识别、环境感知与自主导航等智能应用。然而，在资源受限的边缘设备上直接部署深度学习模型，传统数字计算架构面临内存占用大、算力需求高的问题。本文提出WISE架构，通过无线广播实现模型分拆访问，并在射频端直接进行通用复数矩阵-向量乘法的物理计算，突破能效瓶颈。基于软件定义无线电平台，无线传输模型参数，实验表明该方法在客户端实现95.7%的图像分类准确率，操作功耗低至6.0 fJ/MAC，计算效率达165.8 TOPS/W，相较传统数字计算提升两个数量级以上。该方案为无线连接边缘设备提供了高效深度学习推理新路径。
原文摘要 · Abstract (English)
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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