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Proceedings Paper

Photonic convolutional processor for network edge computing (Conference Presentation)

Paper Abstract

Performing feature extractions in convolution neural networks for deep-learning tasks is computational expensive in electronics. Fourier optics allows convolutional filtering via dot-product multiplication in the Fourier domain similar to the distributive law in mathematics. Here we experimentally demonstrate convolutional filtering exploiting massive parallelism (10^6 channels, 8-bit at 1kHz) of digital mirror display technology, thus enabling 250 TMAC/s. An FPGA-PCIe board controls the ‘weights’ and handles the data I/O, whereas a high-speed camera detects the inverse-Fourier transformed (2nd lens) data. Gen-1 processes with a total delay (including I/O) of ~1ms, while Gen-2 at 1-10ns leveraging integrated photonics at 10GHz and changing the front-end I/O to a joint-transform-correlator (JTC). These processors are suited for image/pattern recognition, super resolution for geolocalization, or real-time processing in autonomous vehicles or military decision making.

Paper Details

Date Published: 9 March 2020
PDF
Proc. SPIE 11299, AI and Optical Data Sciences, 112990K (9 March 2020); doi: 10.1117/12.2545970
Show Author Affiliations
Mario Miscuglio, The George Washington Univ. (United States)
Puneet Gupta, Univ. of California, Los Angeles (United States)
Aydin Babakhani, Univ. of California, Los Angeles (United States)
Chee Wei Wong, Univ. of California, Los Angeles (United States)
Hamed Dalir, Omega Optics, Inc. (United States)
Tarek El-Ghazawi, The George Washington Univ. (United States)
Volker J. Sorger, The George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 11299:
AI and Optical Data Sciences
Bahram Jalali; Ken-ichi Kitayama, Editor(s)

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