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High-clockrate free-space optical in-memory computing

delete2026-02-13
delete0
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OA
AI
Y
Yuanhao Liang
J
James Wang
K
Kaiwen Xue
X
Xinyi Ren
R
Ran Yin
S
Shaoyuan Ou
L
Lian Zhou
Y
Yuan Li
T
Tobias Heuser
N
Niels Heermeier
I
Ian Christen
J
James A. Lott
S
Stephan Reitzenstein
M
Mengjie Yu
Z
Zaijun Chen *
DOI：10.1038/s41377-026-02206-8delete
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摘要

摘要

En 中文
实时处理和执行数据的能力对于从自动驾驶车辆、三维环境传感到远程机器人等应用日益关键。然而，在边缘设备上部署深度神经网络（DNNs）受到缺乏高效能可扩展计算硬件的限制。本文介绍了一种扇出空间飞行时间光神经网络（FAST-ONN），其能够以超低延迟和功耗每秒计算数十亿次卷积运算。这得益于将用于输入调制的垂直腔面发射激光器（VCSEL）高速密集阵列与高像素计数空间光调制器相结合，实现内存内加权。在三维光学系统中，并行差分读出允许单次拍摄实现带符号权重值的精确推理。性能基准测试采用You-Only-Look-Once（YOLO）的特征提取，卷积运算速度达到每秒1亿次（MFPS），并通过光子可重编程性实现系统内反向传播训练。VCSEL发射器可集成到任何自由空间光学计算系统中，以将时钟频率提升至超过吉赫兹，其中设备数量和通道并行性的高可扩展性为扩展自由空间计算硬件提供了新途径。
Keyword:
NEURAL-NETWORK
INFERENCE

期刊

L
Light-Science & Applications
IF:
23.4
论文数:
97
被引数:
0

机构

U
university of southern california
学者数:
4.7W
论文数: 3.8W
被引数: 51
U
university of california berkeley
学者数:
1.4K
论文数: 811
被引数: 0
University of California System 封面图
University of California System
学者数:
37.7W
论文数: 33.8W
被引数: 6.6K
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