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Attojoule Scale Computation of Large Optical Neural Networks
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Dissertation
Attojoule Scale Computation of Large Optical Neural Networks
2019
Overview
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.
Publisher
ProQuest Dissertations & Theses
Subject
