Real-time multi-task diffractive deep neural networks via hardware-software co-design

Real-time multi-task diffractive deep neural networks via hardware-software co-design

Abstract Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantage...

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Autores principales: Yingjie Li, Ruiyang Chen, Berardi Sensale-Rodriguez, Weilu Gao, Cunxi Yu
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
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Acceso en línea:https://doaj.org/article/f70eb5ef627d42bea283df2da64a8c8e
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spelling oai:doaj.org-article:f70eb5ef627d42bea283df2da64a8c8e2021-12-02T15:00:40ZReal-time multi-task diffractive deep neural networks via hardware-software co-design10.1038/s41598-021-90221-72045-2322https://doaj.org/article/f70eb5ef627d42bea283df2da64a8c8e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90221-7https://doaj.org/toc/2045-2322Abstract Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D2NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables first-of-its-like real-time multi-task learning in D22NNs that automatically recognizes which task is being deployed in real-time. Our experimental results demonstrate significant improvements in versatility, hardware efficiency, and also demonstrate and quantify the robustness of proposed multi-task D2NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task.Yingjie LiRuiyang ChenBerardi Sensale-RodriguezWeilu GaoCunxi YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Yingjie Li
Ruiyang Chen
Berardi Sensale-Rodriguez
Weilu Gao
Cunxi Yu
Real-time multi-task diffractive deep neural networks via hardware-software co-design
description Abstract Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D2NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables first-of-its-like real-time multi-task learning in D22NNs that automatically recognizes which task is being deployed in real-time. Our experimental results demonstrate significant improvements in versatility, hardware efficiency, and also demonstrate and quantify the robustness of proposed multi-task D2NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task.
format article
author Yingjie Li
Ruiyang Chen
Berardi Sensale-Rodriguez
Weilu Gao
Cunxi Yu
author_facet Yingjie Li
Ruiyang Chen
Berardi Sensale-Rodriguez
Weilu Gao
Cunxi Yu
author_sort Yingjie Li
title Real-time multi-task diffractive deep neural networks via hardware-software co-design
title_short Real-time multi-task diffractive deep neural networks via hardware-software co-design
title_full Real-time multi-task diffractive deep neural networks via hardware-software co-design
title_fullStr Real-time multi-task diffractive deep neural networks via hardware-software co-design
title_full_unstemmed Real-time multi-task diffractive deep neural networks via hardware-software co-design
title_sort real-time multi-task diffractive deep neural networks via hardware-software co-design
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/f70eb5ef627d42bea283df2da64a8c8e
work_keys_str_mv AT yingjieli realtimemultitaskdiffractivedeepneuralnetworksviahardwaresoftwarecodesign
AT ruiyangchen realtimemultitaskdiffractivedeepneuralnetworksviahardwaresoftwarecodesign
AT berardisensalerodriguez realtimemultitaskdiffractivedeepneuralnetworksviahardwaresoftwarecodesign
AT weilugao realtimemultitaskdiffractivedeepneuralnetworksviahardwaresoftwarecodesign
AT cunxiyu realtimemultitaskdiffractivedeepneuralnetworksviahardwaresoftwarecodesign
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