Broadband radio-frequency signal processing with neuromorphic photonics

Broadband radio-frequency signal processing with neuromorphic photonics
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利用神经形态光子学进行宽带射频信号处理

DOI:
10.1117/12.2614131
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发表时间:
2022
期刊:
AI and Optical Data Sciences III
影响因子:
--
通讯作者:
Prucnal, Paul R.
Prucnal, Paul R.
中科院分区:
--
文献类型:
--
作者:
Blow, Eric C.;Ferreira de Lima, Thomas;Peng, Hsuan-Tung;Zhang, Weipeng;Huang, Chaoran;Shastri, Bhavin J.;Prucnal, Paul R.

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微波光子学和神经形态光子学是两个并行的研究领域,它们同时出现在下一代处理器的前沿。这些领域,虽然最初是独立的,但自然会融合到一个组合的硅光子平台。光学处理方法产生宽带宽、低延迟和密集互连。这些光子系统能够支持以前不可行的应用。系统如光子消除器、光子盲源分离、用于RF指纹识别的光子递归神经网络和用于非线性色散补偿的光子神经网络。本文将重点关注微波光子学和神经形态光子学向RF优化机器学习解决方案的融合。此外,本文还研究了神经形态光子前端的射频噪声性能。结果表明RF性能较差,因此建议采用平衡线性前端来降低噪声系数。
Microwave photonics and neuromorphic photonics are two parallel research areas which have simultaneously emerged at the forefront of next generation processors. These fields, while initially independent, are naturally converging to a combined silicon photonic platform. An optical processing approach yields wide bandwidth, low latency, and dense interconnection. These photonic systems are capable of supporting applications previously unfeasible. Systems such as photonic cancellers, photonic blind source separation, photonic recurrent neural networks for RF fingerprinting, and photonic neural networks for nonlinear dispersion compensation. This paper will focus on the convergence of microwave photonics and neuromorphic photonics towards an RF optimized machine learning solution. Additionally, this paper investigated the RF noise performance of neuromorphic photonic front-end. The results indicated poor RF performances, leading to the proposal of a balanced linear front-end for noise figure reduction.
模拟光链路性能优化:首先最小化噪声系数
DOI: --
发表时间: 2014
期刊: International Topical Meeting on Microwave Photonics
影响因子: --
作者:
E. Ackerman;C. Cox
通讯作者: C. Cox
DOI: 10.1038/s41928-021-00661-2
发表时间: 2021-11-22
期刊: NATURE ELECTRONICS
影响因子: 34.3
作者:
Huang, Chaoran;Fujisawa, Shinsuke;Prucnal, Paul R.
通讯作者: Prucnal, Paul R.
DOI: 10.1109/jstqe.2019.2931252
发表时间: 2020-01-01
影响因子: 4.9
作者:
de Lima, Thomas Ferreira;Tait, Alexander N.;Prucnal, Paul R.
通讯作者: Prucnal, Paul R.
通过集成光子学实现宽带盲源分离
DOI: 10.1109/ipc48725.2021.9593033
发表时间: 2021
期刊: IEEE Photonics Conference (IPC
影响因子: --
作者:
Zhang, Weipeng;Huang, Chaoran;Shastri, Bhavin J.;Prucnal, Paul
通讯作者: Prucnal, Paul