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US Patent 11017309 Optical Ising machines and optical convolutional neural networks

Patent 11017309 was granted and assigned to Massachusetts Institute of Technology on May, 2021 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Massachusetts Institute of Technology
Massachusetts Institute of Technology
Current Assignee
Massachusetts Institute of Technology
Massachusetts Institute of Technology
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11017309
Date of Patent
May 25, 2021
Patent Application Number
16032737
Date Filed
July 11, 2018
Patent Citations
‌
US Patent 10268232 Apparatus and methods for optical neural network
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US Patent 10608663 Real-number photonic encoding
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US Patent 10634851 Apparatus, systems, and methods for nonblocking optical switching
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US Patent 10359272 Programmable photonic processing
Patent Citations Received
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US Patent 12086717 Devices and methods employing optical-based machine learning using diffractive deep neural networks
0
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US Patent 11521055 Optical synapse
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US Patent 12001946 Optoelectronic computing systems
0
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US Patent 12073315 Optoelectronic computing systems
0
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US Patent 11507818 Optoelectronic computing systems
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US Patent 11604978 Large-scale artificial neural-network accelerators based on coherent detection and optical data fan-out
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US Patent 11626931 Photonic processing systems and methods
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US Patent 11281972 Optoelectronic computing systems
...
Patent Primary Examiner
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William R Alexander
Patent abstract

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.

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