Pruning and optimization of optical neural network as a binary optical trigger
Optical neural networks implemented with Mach-Zehnder Interferometer (MZI) arrays are a promising solution to enable fast and energy-efficient machine learning inference, yet finding a practical application has proven challenging due to sensitivity to thermal noise and loss. To leverage the distinct...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Advanced Optical Technologies |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/aot.2024.1501208/full |
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| author | Bokun Zhao Xuening Dong Kaveh Rahbardar Mojaver Brett H. Meyer Odile Liboiron-Ladouceur |
| author_facet | Bokun Zhao Xuening Dong Kaveh Rahbardar Mojaver Brett H. Meyer Odile Liboiron-Ladouceur |
| author_sort | Bokun Zhao |
| collection | DOAJ |
| description | Optical neural networks implemented with Mach-Zehnder Interferometer (MZI) arrays are a promising solution to enable fast and energy-efficient machine learning inference, yet finding a practical application has proven challenging due to sensitivity to thermal noise and loss. To leverage the distinct advantages of integrated optical processors while avoiding its shortcomings given the current state of optical computing, we propose the binary optical trigger as a promising field of application. Implementable as small-scale application-specific circuitry on edge devices, the binary trigger runs binary classification tasks and output binary signals to decide if a subsequent energy intensive system should activate. Motivated by the limited task complexity, constrained area and power budgets of binary triggers, we perform 1) systematic, application-specific hardware pruning by physically removing specific MZIs, and 2) application-specific optimizations in the form of false negative reduction and weight quantization, as well as 3) sensitivity studies capturing the effect of imperfections in real optical components. The result is a customized MZI-mesh topology, MiniBokun Mesh, whose structure provides adequate performance and robustness for a targeted task complexity. We demonstrate in simulation that the pruning methodology achieves at least 50% less MZI usage compared to Clements and Reck meshes with the same input size, translating to at least between 4.6% and 24.2% savings in power consumption and a 40% reduction in physical circuitry footprint compared to other proposed unitary MZI topologies, sacrificing only 1%–2% drop in inference accuracy. |
| format | Article |
| id | doaj-art-52bea04fd8f247309bf386020b5486c0 |
| institution | Kabale University |
| issn | 2192-8584 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Advanced Optical Technologies |
| spelling | doaj-art-52bea04fd8f247309bf386020b5486c02025-01-07T06:47:03ZengFrontiers Media S.A.Advanced Optical Technologies2192-85842025-01-011310.3389/aot.2024.15012081501208Pruning and optimization of optical neural network as a binary optical triggerBokun ZhaoXuening DongKaveh Rahbardar MojaverBrett H. MeyerOdile Liboiron-LadouceurOptical neural networks implemented with Mach-Zehnder Interferometer (MZI) arrays are a promising solution to enable fast and energy-efficient machine learning inference, yet finding a practical application has proven challenging due to sensitivity to thermal noise and loss. To leverage the distinct advantages of integrated optical processors while avoiding its shortcomings given the current state of optical computing, we propose the binary optical trigger as a promising field of application. Implementable as small-scale application-specific circuitry on edge devices, the binary trigger runs binary classification tasks and output binary signals to decide if a subsequent energy intensive system should activate. Motivated by the limited task complexity, constrained area and power budgets of binary triggers, we perform 1) systematic, application-specific hardware pruning by physically removing specific MZIs, and 2) application-specific optimizations in the form of false negative reduction and weight quantization, as well as 3) sensitivity studies capturing the effect of imperfections in real optical components. The result is a customized MZI-mesh topology, MiniBokun Mesh, whose structure provides adequate performance and robustness for a targeted task complexity. We demonstrate in simulation that the pruning methodology achieves at least 50% less MZI usage compared to Clements and Reck meshes with the same input size, translating to at least between 4.6% and 24.2% savings in power consumption and a 40% reduction in physical circuitry footprint compared to other proposed unitary MZI topologies, sacrificing only 1%–2% drop in inference accuracy.https://www.frontiersin.org/articles/10.3389/aot.2024.1501208/fulloptical neural networkMach-Zehnder interferometerpruningedge computingevent-based trigger |
| spellingShingle | Bokun Zhao Xuening Dong Kaveh Rahbardar Mojaver Brett H. Meyer Odile Liboiron-Ladouceur Pruning and optimization of optical neural network as a binary optical trigger Advanced Optical Technologies optical neural network Mach-Zehnder interferometer pruning edge computing event-based trigger |
| title | Pruning and optimization of optical neural network as a binary optical trigger |
| title_full | Pruning and optimization of optical neural network as a binary optical trigger |
| title_fullStr | Pruning and optimization of optical neural network as a binary optical trigger |
| title_full_unstemmed | Pruning and optimization of optical neural network as a binary optical trigger |
| title_short | Pruning and optimization of optical neural network as a binary optical trigger |
| title_sort | pruning and optimization of optical neural network as a binary optical trigger |
| topic | optical neural network Mach-Zehnder interferometer pruning edge computing event-based trigger |
| url | https://www.frontiersin.org/articles/10.3389/aot.2024.1501208/full |
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