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Photonic Neural Networks for Ultrafast Neural Information Processing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Photonic Neural Networks for Ultrafast Neural Information Processing./
作者:	Peng, Hsuan-Tung.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	158 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
標題:	Optics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968547
ISBN:	9798426816961

Photonic Neural Networks for Ultrafast Neural Information Processing.
Peng, Hsuan-Tung.

Photonic Neural Networks for Ultrafast Neural Information Processing. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 158 p.

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--Princeton University, 2022.

This item must not be sold to any third party vendors.

Photonic neural networks (PNNs) represent an important class of optical computing with the goal of producing an accelerated processor that combines the information processing capacity of neuromorphic systems, and the speed and bandwidth of photonics. This thesis focuses on system design, experimental demonstration and AI applications of PNNs using integrated photonics. Two main thrusts of the PNNs development in this thesis are: studying bio-inspired spiking network on InP-based integrated photonic circuits, and building scalable continuous-time neural network using silicon photonics.Toward the first thrust, we study the temporal dynamics of an integrated excitable laser, and demonstrate its analogy to a biological spiking neuron and its compatibility for large-scale system integration. With a solid experimental demonstration, we further propose the model of such photonic spiking neural network, and show its applications including temporal XOR task, time series processing, and recommendation systems. For the second thrust, we investigate a silicon photonics-based system to achieve both precise weight control and programmable nonlinearity. We further explore its application to real-world problems in communication systems. The proposed compact model using silicon photonic recurrent neural network enables real-time specific emitter identification, and provides a promising platform for future edge AI systems.

ISBN: 9798426816961Subjects--Topical Terms:

517925
Optics.
Subjects--Index Terms:

Neuromorphic photonics

Photonic Neural Networks for Ultrafast Neural Information Processing.
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520 $a Photonic neural networks (PNNs) represent an important class of optical computing with the goal of producing an accelerated processor that combines the information processing capacity of neuromorphic systems, and the speed and bandwidth of photonics. This thesis focuses on system design, experimental demonstration and AI applications of PNNs using integrated photonics. Two main thrusts of the PNNs development in this thesis are: studying bio-inspired spiking network on InP-based integrated photonic circuits, and building scalable continuous-time neural network using silicon photonics.Toward the first thrust, we study the temporal dynamics of an integrated excitable laser, and demonstrate its analogy to a biological spiking neuron and its compatibility for large-scale system integration. With a solid experimental demonstration, we further propose the model of such photonic spiking neural network, and show its applications including temporal XOR task, time series processing, and recommendation systems. For the second thrust, we investigate a silicon photonics-based system to achieve both precise weight control and programmable nonlinearity. We further explore its application to real-world problems in communication systems. The proposed compact model using silicon photonic recurrent neural network enables real-time specific emitter identification, and provides a promising platform for future edge AI systems.
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