Attojoule scale computation of large optical neural networks
Author(s)
Sludds, Alexander(Alexander J.)
Download1128817109-MIT.pdf (7.261Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Dirk Englund.
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Show full item recordAbstract
The ultra-high bandwidth and low energy cost of modern photonics offers many opportunities for improving both speed and energy efficiency in classical information processing. Recently a new architecture has been proposed which allows for substantial energy reductions in matrix-matrix products by utilizing balanced homodyne detection for computation and optical fan-out for data delivery. In this thesis I work towards the analysis and implementation of both analog and digital optical neural networks. For analog optical neural networks I discuss both the physical implementation of this system as well as an analysis of limits imposed on this system by shot noise, crosstalk, and electro-optic/opto-electronic information conversion. From these results, it is found that femtojoule-scale computation per multiply and accumulate operation is achievable in the near term with further energy gains foreseeable with emerging technology. This thesis also presents a system-scale throughput and energy analysis of digital optical neural networks, which can enable incredibly high data speeds (> 10GHz) with CMOS compatible voltages at weight transmitter power dissipation comparable to a modern CPU.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 61-70).
Date issued
2019Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.