通过在无线电频率的物理计算进行分类机器学习
Zhihui Gao1, Sri Krishna Vadlamani2, Kfir Sulimany2
1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
Science advances
|January 9, 2026
概括
我们介绍了WISE,这是一个无线边缘网络架构,可以在边缘设备上实现高效的机器学习推断. 智能显著降低了人工智能任务的能源消耗,超过了GPU.
科学领域:
- 计算机工程 计算机工程
- 无线通信无线通信
- 机器学习 机器学习
背景情况:
- 边缘设备需要高效的机器学习来实现智能应用.
- 传统的数字计算架构面临着边缘AI的内存和功率限制.
- 资源受限的边缘设备限制了实时机器学习推断.
研究的目的:
- 介绍WISE,一个用于无线边缘网络的新型计算架构.
- 在多个边缘设备上实现高效和同时的机器学习推断.
- 为了减少机器学习计算在边缘的能源消耗.
主要方法:
- 开发了WISE,这是用于无线边缘网络的计算架构.
- 通过无线无线广播实现分类模型访问.
- 在物理中使用复杂值矩阵向量乘法在无线电频率的计算.
- 使用软件定义的无线电平台进行实验.
主要成果:
- 实现了95.7%的图像分类准确度和97.2%的音频分类准确度.
- 证明的超低能耗:图像分类 6.0 fJ/MAC,音频分类 2.8 fJ/MAC.
- 与传统数字计算 (例如GPU) 相比,能源效率提高了10倍以上.
结论:
- 智慧为机器学习的边缘计算提供了显著的进步.
- 拟议的架构克服了边缘AI传统数字计算的局限性.
- 在资源受限的无线边缘设备上,WISE可实现高度准确和节能的人工智能推断.
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