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Proceedings Paper

Optical convolution based computational method for low-power image processing
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Paper Abstract

Convolutional neural networks (CNNs) is becoming a critical role for deep learning-based computer vision applications. Through CNN we can extract meaningful information out of massive sensor data. Vision applications use this information to analyze the mainstream trends of the data and take immediate action based on these trends. However, CNN's energy consumption and bandwidth limitations make it difficult for CNN network systems to deploy in mobile systems with stringent energy limits. In this paper, we explore the simulation and the hardware method of optical convolution for low power image processing, which is inspired by the bio-image sensor Angle sensitive pixel (ASP). This optical computation method may be used to substitute the first convolution layer of the CNN network due to its energy-saving features and speed of light processing time. We adopt two-layer of customized transmission grating to perform this optical convolution computing. By generating the Talbot effect, the two-layer grating structure can perform optical convolution computation using Gabor wavelet filters, which will cause zero electrical power. We demonstrate both simulation and experiment results for optical convolution through our algorithm and prototype system, the convolution results can extract different meaningful information about the original image, which is very similar to edge filtering. This optical operation will hopefully be used to replace the first convolution layer of CNN since it can effectively reduce both the consumption of the energy power and the performing time.

Paper Details

Date Published: 6 September 2019
PDF: 12 pages
Proc. SPIE 11136, Optics and Photonics for Information Processing XIII, 111360N (6 September 2019);
Show Author Affiliations
Yi Huang, Zhejiang Univ. (China)
Yuan Chen, Zhejiang Univ. (China)
Zhentao Qin, Zhejiang Univ. (China)
Jinlei Zhang, Zhejiang Univ. (China)
Zhenrong Zheng, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 11136:
Optics and Photonics for Information Processing XIII
Khan M. Iftekharuddin; Abdul A. S. Awwal; Victor H. Diaz-Ramirez; Andrés Márquez, Editor(s)

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