高时钟率的自由空间光学内存计算
Yuanhao Liang1,2, James Wang1,2, Kaiwen Xue1,2
1Department of Electrical Engineering and Computing Sciences, University of California, Berkeley, CA, 94720, USA.
Light, science & applications
|February 12, 2026
概括
我们开发了一种新的光学神经网络 (FAST-ONN),用于节能边缘计算. 该系统每秒实现数十亿次计算,为高级应用程序实现实时处理.
科学领域:
- 光电学是指光电子产品.
- 光学计算是指光学计算.
- 人工智能 硬件 硬件
背景情况:
- 实时数据处理对于自主系统和机器人技术至关重要.
- 当前的边缘设备在与深度神经网络 (DNN) 的功率和可扩展性需求作斗争.
研究的目的:
- 引入一个新的外观空间飞行时间光学神经网络 (FAST-ONN).
- 为边缘DNN展示一个节能和可扩展的硬件解决方案.
主要方法:
- 使用垂直腔表面发射激光器 (VCSEL) 进行高速输入调制.
- 使用高像素数的空间光调节器进行内存权重.
- 实现了一个3D光学系统,并行差分读取用于签名重量推断.
主要成果:
- 在超低延迟和功耗的情况下,每秒实现数十亿次卷积.
- 基准性能与你只看一次 (YOLO) 功能提取在每秒1000万 (MFPS).
- 在系统内证明了具有光子可重编程性的向后传播训练.
结论:
- FAST-ONN提供了一个可扩展的解决方案,用于在边缘设备上节能部署DNN.
- VCSEL发射器可以提高超出千兆赫的自由空间光学计算时钟速率.
- 该技术为扩大自由空间计算硬件开辟了新的可能性.
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