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公开(公告)号：US11334107B2
公开(公告)日：2022-05-17
申请号：US16986383
申请日：2020-08-06
发明人： Jacques Johannes Carolan , Mihika Prabhu , Scott A. Skirlo , Yichen Shen , Marin Soljacic , Dirk Englund , Nicholas Christopher Harris
IPC分类号： G06E3/00 , G06N3/04 , G06N3/08 , G02F1/225 , G02F1/35 , G02F1/365 , G02F3/02 , G06N3/067 , G02F1/21
摘要： An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.
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公开(公告)号：US20220012619A1
公开(公告)日：2022-01-13
申请号：US17239830
申请日：2021-04-26
发明人： Charles ROQUES-CARMES , Yichen Shen , Li JING , Tena DUBCEK , Scott A. SKIRLO , Hengameh BAGHERIANLEMRASKI , Marin SOLJACIC
摘要： 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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公开(公告)号：US20190294199A1
公开(公告)日：2019-09-26
申请号：US16273257
申请日：2019-02-12
发明人： Jacques Johannes Carolan , Mihika Prabhu , Scott A. Skirlo , Yichen Shen , Marin Soljacic , Nicholas Christopher Harris , Dirk Englund
摘要： An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.
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公开(公告)号：US10785858B2
公开(公告)日：2020-09-22
申请号：US15014401
申请日：2016-02-03
发明人： Ido Kaminer , Liang Jie Wong , Ognjen Ilic , Yichen Shen , John Joannopoulos , Marin Soljacic
IPC分类号： H05G2/00
摘要： An apparatus includes at least one conductive layer, an electromagnetic (EM) wave source, and an electron source. The conductive layer has a thickness less than 5 nm. The electromagnetic (EM) wave source is in electromagnetic communication with the at least one conductive layer and transmits a first EM wave at a first wavelength in the at least one conductive layer so as to generate a surface plasmon polariton (SPP) field near a surface of the at least one conductive layer. The electron source propagates an electron beam at least partially in the SPP field so as to generate a second EM wave at a second wavelength less than the first wavelength.
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公开(公告)号：US10268232B2
公开(公告)日：2019-04-23
申请号：US15612043
申请日：2017-06-02
发明人： Nicholas Christopher Harris , Jacques Johannes Carolan , Mihika Prabhu , Dirk Robert Englund , Scott A. Skirlo , Yichen Shen , Marin Soljacic
摘要： An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.
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公开(公告)号：US11914415B2
公开(公告)日：2024-02-27
申请号：US17736667
申请日：2022-05-04
发明人： Jacques Johannes Carolan , Mihika Prabhu , Scott A. Skirlo , Yichen Shen , Marin Soljacic , Dirk Englund , Nicholas C. Harris
IPC分类号： G06E3/00 , G06N3/04 , G06N3/084 , G02F1/225 , G02F1/35 , G02F1/365 , G02F3/02 , G06N3/067 , G06N3/08 , G02F1/21
CPC分类号： G06E3/005 , G02F1/225 , G02F1/3526 , G02F1/365 , G02F3/024 , G06E3/006 , G06E3/008 , G06N3/04 , G06N3/0675 , G06N3/08 , G06N3/084 , G02F1/212 , G02F2202/32 , G02F2203/15
摘要： An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.
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公开(公告)号：US11017309B2
公开(公告)日：2021-05-25
申请号：US16032737
申请日：2018-07-11
发明人： Charles Roques-Carmes , Yichen Shen , Li Jing , Tena Dubcek , Scott A. Skirlo , Hengameh Bagherianlemraski , Marin Soljacic
摘要： 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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公开(公告)号：US20230045938A1
公开(公告)日：2023-02-16
申请号：US17736667
申请日：2022-05-04
发明人： Jacques Johannes CAROLAN , Mihika PRABHU , Scott A. SKIRLO , Yichen Shen , Marin SOLJACIC , DIRK ENGLUND , Nicholas C. HARRIS
摘要： An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.
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公开(公告)号：US20200379504A1
公开(公告)日：2020-12-03
申请号：US16986383
申请日：2020-08-06
发明人： Jacques Johannes CAROLAN , Mihika PRABHU , Scott A. SKIRLO , Yichen Shen , Marin SOLJACIC , DIRK ENGLUND , Nicholas Christopher HARRIS
摘要： An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.
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公开(公告)号：US10768659B2
公开(公告)日：2020-09-08
申请号：US16273257
申请日：2019-02-12
发明人： Jacques Johannes Carolan , Mihika Prabhu , Scott A. Skirlo , Yichen Shen , Marin Soljacic , Nicholas Christopher Harris , Dirk Englund
IPC分类号： G06E3/00 , G06N3/04 , G06N3/08 , G02F1/225 , G02F1/35 , G02F1/365 , G02F3/02 , G06N3/067 , G02F1/21
摘要： An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.
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