University of Central Florida Research Foundation Inc

Rensselaer Polytechnic Institute

Shuo Pang

Guifang Li

Ge Wang

Wenxiang Cong

Zaifeng Shi

Axel Schulzgen

He Wen

Jian Zhao

Zheyuan Zhu

Yangyang Sun

Photonic units for vector-vector multiplication, matrix-vector multiplication, matrix-matrix multiplication, batch matrix-matrix multiplication, and tensor-tensor multiplication are described. Multiplications are through coherent mixing and square-law detection. There are many dimensions—wavelength, vector mode, quadrature, and three dimensions of space—that can be used to construct photonic accelerators. The encoded input vector or input matrix is fanned out into a desired number of copies and mixed with the corresponding encoded local oscillators containing the weight vectors comprising the weight matrix. Any subset of two (three) dimensions can be used to construct photonic accelerators for matrix-vector (matrix-matrix) multiplications. Multiple dimensions can be combined into a hyperdimension to increase the scalability. Each dimension, each non-overlapping subset of a dimension, or each non-overlapping subset of a hyperdimension, can be used independently to construct a photonic tensor accelerator (PTA) for batch matrix multiplication, or tensor multiplication operations.

Photonic tensor accelerators for artificial neural networks

An adaptive system and a method for adjusting a parameter in the adaptive system includes operating an adaptive system with an output signal produced from an input signal applied to an input, in which a parameter with a finite range is determined based on a difference between the output signal and a target output signal. In one example the parameter with the finite range is a fixed-point parameter or an analog parameter. The parameter is accessed from the adaptive system. A probability distribution of the parameter is assigned. The finite range for the parameter is updated based on the probability distribution which has been assigned. The probability distribution function may be updated alone with the finite range of the parameter. The probability distribution may be derived from one or more historical values of the parameter, and a plurality of system parameters belonging to an identical category of data as the parameter.

Adjusting parameters in an adaptive system

A flexible, artifact-free, and lensless fiber-based imaging system for biological objects. This system combines image reconstruction by a trained deep neural network with low-loss image transmission through disordered glass-air Anderson localized optical fiber. High quality images of biological objects can be obtained using short (few centimeters) or long (more than one meter) segments of disordered fiber with and without fiber bending. The deep neural network can also be designed to perform image classification. The system provides the unique property that the training performed within a straight fiber setup can be utilized for high fidelity reconstruction/classification of images that are transported through either straight or bent fiber making retraining for different bending situations unnecessary. In addition, high quality image transport and reconstruction is demonstrated for cells that are several millimeters away from the fiber input facet eliminating the need for additional optical elements at the distal end of the fiber. This novel imaging system shows great potential for practical applications in endoscopy including studies on freely behaving subjects.

Fiber imaging apparatus, methods, and applications

Novel and advantageous systems and methods for performing X-ray imaging by extracting X-ray phase-shift and/or dark-field information through a detector that has built-in G2 functionality are provided. Grating translation can be replaced by an electrical operation in the detection procedure, thereby eliminating the need for the analyzer grating and the typical mechanical stepping process.

X-ray phase contrast and dark-field information extraction with electric fringe scanning and/or active pixel processing

Growing community of inventors

Oviedo, FL, United States of America

Shuo Pang