Optical convolution based computational method for low-power image processing

Yi Huang; Yuan Chen; Zhentao Qin; Jinlei Zhang; Zhenrong Zheng

doi:10.1117/12.2527733

6 September 2019 Optical convolution based computational method for low-power image processing

Yi Huang, Yuan Chen, Zhentao Qin, Jinlei Zhang, Zhenrong Zheng

Proceedings Volume 11136, Optics and Photonics for Information Processing XIII; 111360N (2019) https://doi.org/10.1117/12.2527733
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Yi Huang, Yuan Chen, Zhentao Qin, Jinlei Zhang, and Zhenrong Zheng "Optical convolution based computational method for low-power image processing", Proc. SPIE 11136, Optics and Photonics for Information Processing XIII, 111360N (6 September 2019); https://doi.org/10.1117/12.2527733

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available