Presentation
17 September 2018 Residue number system arithmetic based on integrated nanophotonics (Conference Presentation)
Author Affiliations +
Abstract
Residue number system (RNS) enables dimensionality reduction of an arithmetic problem by representing a large number as a set of smaller integers, where the number is decomposed by prime number factorization. These reduced problem sets can then be processed in- dependently and in parallel, thus improving computational efficiency and speed. Here we show an optical RNS hardware representation based on integrated nanophotonics. The digit-wise shifting in RNS arithmetic is expressed as spatial routing of an optical signal in 2×2 hybrid photonic-plasmonic switches. Here the residue is represented by spatially shifting the input waveguides relative to the routers’ outputs, where the moduli are represented by the number of waveguides. By cascading the photonic 2×2 switches, we design a photonic RNS adder and a multiplier forming an all-to- all sparse directional network. The advantage of this photonic arithmetic processor is the short (10’s ps) computational execution time given by the optical propagation delay through the integrated nanophotonic router. Furthermore, we show how photonic processing in- the-network leverages the natural parallelism of optics such as wavelength-division-multiplexing in this RNS processor. A key application for such a photonic RNS engine is the functional analysis of convolutional neural networks.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jiaxin Peng, Shuai Sun, Tarek El-Ghazawi, and Volker J. Sorger "Residue number system arithmetic based on integrated nanophotonics (Conference Presentation)", Proc. SPIE 10751, Optics and Photonics for Information Processing XII, 107510C (17 September 2018); https://doi.org/10.1117/12.2322079
Advertisement
Advertisement
KEYWORDS
Integrated nanophotonics

Radon

Integrated optics

Switches

Waveguides

Convolutional neural networks

Functional analysis

RELATED CONTENT


Back to Top