Photonic convolutional processor for network edge computing (Conference Presentation)

Mario Miscuglio; Puneet Gupta; Aydin Babakhani; Chee Wei Wong; Hamed Dalir; Tarek El-Ghazawi; Volker J. Sorger

doi:10.1117/12.2545970

9 March 2020 Photonic convolutional processor for network edge computing (Conference Presentation)

Mario Miscuglio, Puneet Gupta, Aydin Babakhani, Chee Wei Wong, Hamed Dalir, Tarek El-Ghazawi, Volker J. Sorger

Proceedings Volume 11299, AI and Optical Data Sciences; 112990K (2020) https://doi.org/10.1117/12.2545970
Event: SPIE OPTO, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Mario Miscuglio, Puneet Gupta, Aydin Babakhani, Chee Wei Wong, Hamed Dalir, Tarek El-Ghazawi, and Volker J. Sorger "Photonic convolutional processor for network edge computing (Conference Presentation)", Proc. SPIE 11299, AI and Optical Data Sciences, 112990K (9 March 2020); https://doi.org/10.1117/12.2545970

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