2026-09-20T06:52:41Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1231352026-06-06T00:54:31Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Dirk Englund.
Sludds, Alexander(Alexander J.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-12-05T18:05:23Z
2019-12-05T18:05:23Z
2019
2019
https://hdl.handle.net/1721.1/123135
1128817109
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).
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.
by Alexander Sludds.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
70 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Attojoule scale computation of large optical neural networks
Thesis
application/pdf
2019-12-05T18:05:22Z
Publication
Master
EECS
unknown
unknown
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<Abstract>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.</Abstract>
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