Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only log2 N of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

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.

Abstract: An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only log2 N of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

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.

Abstract: 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.

Abstract: An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only log2 N of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

Abstract: 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.

Abstract: An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only log2 N of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

Abstract: 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.

Abstract: An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only log2 N of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

Abstract: 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.

Abstract: An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only log2 N of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

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.

Abstract: A system for training a neural network model, the neural network model comprising a plurality of layers including a first hidden layer associated with a first set of weights, the system comprising at least one computer hardware processor programmed to perform: obtaining training data; selecting a unitary rotational representation for representing a matrix of the first set weights, the selected unitary rotational representation comprising a plurality of parameters; training the neural network model using the training data using an iterative neural network training algorithm to obtain a trained neural network model, each iteration of the iterative neural network training algorithm comprising: updating values of the plurality of parameters in the selected unitary rotational representation for representing the matrix of the set of weights for the at least one hidden layer, and saving the trained neural network model.

Abstract: An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only log2 N of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

Abstract: 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.