Lightelligence Pte. Ltd.

Massachusetts Institute of Technology

Li Jing

Matthew Raja Khoury

Rumen Rumenov Dangovski

Maurice B Steinman

Yichen Shen

Huaiyu Meng

Cheng-Kuan Lu

Ronald Gagnon

Arash Hosseinzadeh

Jianhua Wu

Peng Xie

Marin Soljacic

Scott A Skirlo

Charles Roques-Carmes

Longwu Ou

Tena Dubcek

Hengameh Bagherianlemraski

Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals. For each of at least two copies of a first subset of one or more optical signals, a corresponding multiplication module multiplies the one or more optical signals of the first subset by one or more matrix element values using optical amplitude modulation. For results of two or more of the multiplication modules, a summation module produces an electrical signal that represents a sum of the results of the two or more of the multiplication modules.

Optoelectronic computing systems

A photonic parallel network can be used to sample combinatorially hard distributions of Ising problems. The photonic parallel network, also called a photonic processor, finds the ground state of a general Ising problem and can probe critical behaviors of universality classes and their critical exponents. In addition to the attractive features of photonic networks—passivity, parallelization, high-speed and low-power—the photonic processor exploits dynamic noise that occurs during the detection process to find ground states more efficiently.

Optical Ising machines and optical convolutional neural networks

A method includes: providing input information in an electronic format; converting the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. For example, a set of input values are encoded on respective optical signals. For each of at least two subsets of optical signals, a copying module splits the subset into multiple copies of the optical signals. For each copy of a first subset of optical signals, a corresponding multiplication module multiplies the optical signals of the first subset by matrix element values using optical amplitude modulation. A summation module produces an electrical signal representing a sum of the results of the multiplication modules.

Data to be processed includes vector element values of an input vector and matrix element values of a model matrix associated with a neural network model. A vector-matrix multiplication module receives a set of matrix element values for performing a vector-matrix multiplication operation. Processing the data includes computing a plurality of intermediate vectors based on element-wise vector multiplication between different subsets of the vector element values and different respective pre-processing vectors. The vector-matrix multiplication module is loaded with a core matrix, and the input vector is multiplied by the model matrix based on separately multiplying each of the intermediate vectors by the loaded core matrix.

Processing matrix operations for rate limited systems

A photonic parallel network can be used to sample combinatorially hard distributions of Ising problems. The photonic parallel network, also called a photonic processor, finds the ground state of a general Ising problem and can probe critical behaviors of universality classes and their critical exponents. In addition to the attractive features of photonic networks&#x2014;passivity, parallelization, high-speed and low-power&#x2014;the photonic processor exploits dynamic noise that occurs during the detection process to find ground states more efficiently.

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Li Jing