2017/11/30 by Jiaxin Peng, Shuai Sun, Peng, Jiaxin +8
Computer Science · Engineering · Physics and Astronomy · #Advanced Surface Polishing Techniques #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Integrated Circuits and Semiconductor Failure Analysis #Near-Field Optical Microscopy #Optics (physics.optics) #cs.ET #physics.optics
paper · pdf · doi:10.48550/arxiv.1712.00049
7 pages, 5 figures
arxiv created 2017/11/30 · openalex publication_date 2017/11/30 · arxiv updated 2017/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 using the moduli as basic functions. These reduced problem sets can then be processed independently 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 2x2 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 2x2 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 or optical angular momentum in this RNS processor. A key application for photonic RNS is the functional analysis convolution with widespread usage in numerical linear algebra, computer vision, language- image- and signal processing, and neural networks.