A system performing optical and/or electro-optical tensor operations and featuring a photonic dot product engine with a first input and a second input and summation to perform multiply-accumulate operations. The first and/or second input is a matrix, and/or a vector, and/or scalar. The system is a Photonic Tensor Core.

A machine vision task, machine learning task, and/or classification of objects is performed using a diffractive optical neural network device. Light from objects passes through or reflects off the diffractive optical neural network device formed by multiple substrate layers. The diffractive optical neural network device defines a trained function between an input optical signal from the object light illuminated at a plurality or a continuum of wavelengths and an output optical signal corresponding to one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types/classes created by optical diffraction and/or reflection through/off the substrate layers. Output light is captured with detector(s) that generate a signal or data that comprise the one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types or object classes which are used to perform the task or classification.

There is provided an optical signal processing device that constitutes a neural network, characterized by including an optical computation device including: a light modulator that converts an electric signal into an optical signal; an optical circuit that converts the optical signal through computation processing on the optical signal which has been modulated by the light modulator, the optical circuit including an optical medium with a controlled distribution of a refractive index corresponding to a weight in the neural network; and a light receiver that obtains an output signal by receiving the optical signal which has been converted by the optical circuit.

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.

A photonics reservoir computing system is described. The system is configured for propagating at least one optical signal so as to create resulting radiation signals in the output channels. The photonics reservoir computing system further comprises weighting elements for weighting signals from the output channels, and at least one optical detector for optically detecting signals from the output channels. The system is adapted for estimating signals from the output channels through an output of the optical detector.

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.

A threat management facility stores a number of entity models that characterize reportable events from one or more entities. A stream of events from compute instances within an enterprise network can then be analyzed using these entity models to detect behavior that is inconsistent or anomalous for one or more of the entities that are currently active within the enterprise network.

Various Reservoir Computing systems and a method performed by a Reservoir Computing system are provided. A Reservoir Computing system includes a laser for emitting light. The Reservoir Computing system further includes a mirror for reflecting external feedback light back to the laser. The Reservoir Computing system also includes a modulator for modulating the external feedback light reflected back to the laser. The Reservoir Computing system additionally includes a photo-detector for converting a laser output signal to an electrical signal. The Reservoir Computing system further includes an analog-to-digital converter for sampling the electrical signal. The Reservoir Computing system also includes a controller for applying a learning algorithm to the sampled electrical signal.

In one aspect, a computational machine includes an optical device configured to receive energy from an optical energy source and generate a number N1 of optical signals, and a number N2 of coupling devices, each of which controllably couples a plurality of the number N1 optical signals. The coupling devices are individually controlled to simulate a computational problem. In another aspect, a computational machine includes a number N1 of parametric oscillators and a number N2 of coupling devices, each of which controllably couples a plurality of the number N1 of parametric oscillators together. The coupling devices are individually controlled to simulate a computational problem.

Vector and matrix multiplications can be accomplished in photonic circuitry by coherently combining light that has been optically modulated, in amplitude and/or phase, in accordance with the vector and matrix components. Disclosed are various beneficial photonic circuit layouts characterized by loss- and delay-balanced optical paths. In various embodiments, loss balancing across paths is achieved with suitable optical coupling ratios and balanced numbers of waveguide crossings (using dummy crossings where needed) across the paths. Delays are balanced in some embodiments with geometrically delay-matched optical paths.