光子贝叶斯神经网络:利用可编程噪音,实现稳健和不确定性意识的计算
Yangyang Zhuge1,2,3, Zhihao Ren1,2,4, Zian Xiao1,2
1Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|April 28, 2025
概括
研究人员开发了一种光子贝叶斯神经网络 (PBNN),利用设备噪声进行强大的计算. 这种新架构增强了光子神经网络 (PNN) 的不确定性意识的应用程序.
科学领域:
- 光子学 是一个光子学.
- 人工智能的人工智能
- 集成电路 集成电路
背景情况:
- 光子集成电路 (Si-PIC) 提供节能,高带宽的光子神经网络 (PNN).
- 确定性PNN与硬件噪音和数据不确定性作斗争,限制了现实世界的应用.
- 目前的PNN缺乏对设备波动的稳定性,无法有效处理数据不确定性.
研究的目的:
- 提出一种新的光子贝叶斯神经网络 (PBNN) 架构.
- 提高PNN的稳定性和不确定性处理能力.
- 将固有的设备噪声转化为计算优势.
主要方法:
- 开发了一个采用贝叶斯原则的PBNN架构.
- 使用基于光子噪声的随机数发生器与马赫-泽恩德干扰仪和微环共振器.
- 利用实验提取的数据进行建模和验证.
主要成果:
- 在手写数字识别方面达到高达98%的分类准确度,与传统模型相匹配.
- 在多式联网数据处理,回归和异常值检测方面证明了PBNN的有效性.
- 展示了一个可扩展和节能的架构,将光子噪声转化为计算价值.
结论:
- 拟议的PBNN架构增强了稳定性,并解决了光子计算中的不确定性.
- 这种方法克服了确定性PNN的局限性,使不确定性意识的应用成为可能.
- PBNN代表了使用光子学在现实世界的人工智能应用中的重大进步.
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