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Universal Analog Computation on Programmable Nanophotonic Integrated Circuits.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Universal Analog Computation on Programmable Nanophotonic Integrated Circuits./
作者:	Pai, Sunil Kochikar.
面頁冊數:	1 online resource (216 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
標題:	Software. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29342319click for full text (PQDT)
ISBN:	9798352606322

Universal Analog Computation on Programmable Nanophotonic Integrated Circuits.
Pai, Sunil Kochikar.

Universal Analog Computation on Programmable Nanophotonic Integrated Circuits. - 1 online resource (216 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

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

Includes bibliographical references

Universal programmable nanophotonic meshes, or networks of Mach-Zehnder interferometers, are an exciting new avenue to arbitrarily shape light beams for energy-efficient chip-scale linear optical matrix multiplication. Applications include sensing, imaging, LIDAR, quantum computing, cryptography, and machine learning. Such devices can be scalably manufactured commercially in semiconductor foundries but can suffer from manufacturing and other systematic errors. In this thesis, I discuss both theoretical and experimental contributions for programmable optical meshes to address these concerns.First, I introduce a new class of binary tree meshes that are more error tolerant than currently proposed architectures. To prove this error tolerance formally, I explain my architecture-dependent error sensitivity theory relating individual component errors to overall system performance. I furthermore discuss several calibration and configuration algorithms aimed at reducing these errors, including self-configuration and "parallel nullification" that minimizes programming time of any feedforward photonic mesh network.Next, I present my design and implementation of a fully packaged 6 x 6 programmable "triangular mesh" outfitted with an experimental optical rig setup in our lab and explore its use as a matrix multiply accelerator for machine learning. As a key application, I demonstrate in situ backpropagation training (the most popular and widely used gradient-based training algorithm for machine learning) directly through optical measurement for the first time on our chip, which agrees well with digital simulations of the training process.Finally, I apply the same chip to study new photonic cryptocurrency and blockchain applications by exploring a paradigm shift: implementing discrete-valued matrix multiplication in a photonic mesh for robust and energy-efficient digitally verifiable computation. In this vein, I propose LightHash, a hash function that incorporates photonic computation into the Bitcoin protocol to secure cryptocurrency transactions. I analyze component error scaling to overall LightHash error rates with size and bit depth of our photonic matrix multiplier and demonstrate new error correction protocols to minimize such error.These experimental achievements coupled with my new theoretical framework could have significant and lasting implications on photonic integrated circuits, programmable optics, major tech sectors for artificial intelligence and cryptography, and beyond.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352606322Subjects--Topical Terms:

619355
Software.
Index Terms--Genre/Form:

542853
Electronic books.

Universal Analog Computation on Programmable Nanophotonic Integrated Circuits.
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502 $a Thesis (Ph.D.)--Stanford University, 2022.
504 $a Includes bibliographical references
520 $a Universal programmable nanophotonic meshes, or networks of Mach-Zehnder interferometers, are an exciting new avenue to arbitrarily shape light beams for energy-efficient chip-scale linear optical matrix multiplication. Applications include sensing, imaging, LIDAR, quantum computing, cryptography, and machine learning. Such devices can be scalably manufactured commercially in semiconductor foundries but can suffer from manufacturing and other systematic errors. In this thesis, I discuss both theoretical and experimental contributions for programmable optical meshes to address these concerns.First, I introduce a new class of binary tree meshes that are more error tolerant than currently proposed architectures. To prove this error tolerance formally, I explain my architecture-dependent error sensitivity theory relating individual component errors to overall system performance. I furthermore discuss several calibration and configuration algorithms aimed at reducing these errors, including self-configuration and "parallel nullification" that minimizes programming time of any feedforward photonic mesh network.Next, I present my design and implementation of a fully packaged 6 x 6 programmable "triangular mesh" outfitted with an experimental optical rig setup in our lab and explore its use as a matrix multiply accelerator for machine learning. As a key application, I demonstrate in situ backpropagation training (the most popular and widely used gradient-based training algorithm for machine learning) directly through optical measurement for the first time on our chip, which agrees well with digital simulations of the training process.Finally, I apply the same chip to study new photonic cryptocurrency and blockchain applications by exploring a paradigm shift: implementing discrete-valued matrix multiplication in a photonic mesh for robust and energy-efficient digitally verifiable computation. In this vein, I propose LightHash, a hash function that incorporates photonic computation into the Bitcoin protocol to secure cryptocurrency transactions. I analyze component error scaling to overall LightHash error rates with size and bit depth of our photonic matrix multiplier and demonstrate new error correction protocols to minimize such error.These experimental achievements coupled with my new theoretical framework could have significant and lasting implications on photonic integrated circuits, programmable optics, major tech sectors for artificial intelligence and cryptography, and beyond.
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