A data processing array comprises a plurality of modules, each with a memory, positioned in an array of rows and columns interconnected by a pooling chain that carries data to and receives data from selected ones or groups of the modules. Each modules can also have light modulator elements for transmitting data as light signals and a light sensor for receiving data in the form of modulated light. Pooling switches in the pooling chain between modules open and close the pooling chain lines for selecting and grouping modules. Analog data lines separate from the pooling chain can also carry data to and from the modules. Pooling control lines connected to the switches turn the switches on and off for the selecting and grouping of modules. Module control lines, also separate from the pooling chain, connected to the modules enable various data input, output, and processing by the memory or other components in the module.

A signal processing apparatus that processes image data output from a photoelectric conversion unit including a light-receiving region and a light-blocking region. The apparatus includes a control data generation unit that outputs control data used to generate correction data for correcting the image data using a trained model generated through machine learning, and a signal processing unit that generates the correction data on the basis of light-blocked image data and the control data, the light-blocked image data being image data, among the image data, that is from the light-blocking region, and corrects light-received image data in accordance with the correction data without applying the trained model, the light-received image data being image data, among the image data, that is from the light-receiving region.

An optical co-processor architecture using array of weak optical perceptron is disclosed in a computing architecture for a neuro-inspired computing platform. The use of weak optical perceptron in this architecture facilitates the manufacturability and use of an exemplary computing microchips having an array of weak-learners in which a plurality of weak-learners of the array are selectively grouped and their outputs are aggregated to provide a coprocessing output for a given computing and decision-making task in an integrated photonic system.

A dual-floating gates optoelectronic self-exciting synaptic memristor includes a bottom gate, a barrier layer coated on a surface of the bottom gate, a quantum dot layer coated on a surface of a middle portion of the barrier layer, two inverted L-shaped electron or hole tunneling layers coated on a surface of two end portions of the quantum dot layer respectively, two inverted L-shaped floating gate storage layers coated on the electron or hole tunneling layers respectively, two electron or hole blocking layers coated on the two floating gate storage layers respectively, an inverted L-shaped source electrode and an inverted L-shaped drain electrode coated on the two electron or hole blocking layers respectively, a photosensitive material layer coated on a surface of a middle portion of the quantum dot layer, and a top gate coated on the photosensitive material layer.

An optical synapse comprises a memristive device for non-volatile storage of a synaptic weight dependent on resistance of the device, and an optical modulator for volatile modulation of optical transmission in a waveguide. The memristive device and optical modulator are connected in control circuitry which is operable, in a write mode, to supply a programming signal to the memristive device to program the synaptic weight and, in a read mode, to supply an electrical signal, dependent on the synaptic weight, to the optical modulator whereby the optical transmission is controlled in a volatile manner in dependence on programmed synaptic weight.

An accelerator for modern convolutional neural networks applies the Winograd filtering algorithm in a wavelength division multiplexing integrated photonics circuit modulated by a memristor-based analog memory unit.

An imaging device connected to a neural network is provided. An imaging device having a neuron in a neural network includes a plurality of first pixels, a first circuit, a second circuit, and a third circuit. Each of the plurality of first pixels includes a photoelectric conversion element. The plurality of first pixels is electrically connected to the first circuit. The first circuit is electrically connected to the second circuit. The second circuit is electrically connected to the third circuit. Each of the plurality of first pixels generates an input signal of the neuron. The first circuit, the second circuit, and the third circuit function as the neuron. The third circuit includes an interface connected to the neural network.

An authentication model dynamically adjusts authentication factors required for access to a remote resource based on changes to a risk score for a user, a device, or some combination of these. For example, the authentication model may conditionally specify the number and type of authentication factors required by a user/device pair, and may dynamically alter authentication requirements based on changes to a current risk assessment for the user/device while the remote resource is in use.

An optical neural network apparatus that optically implements an artificial neural network includes an input layer, a hidden layer, and an output layer sequentially arranged in a traveling direction of light, wherein the output layer includes an image sensor including a plurality of light sensing pixels arranged in two dimensions, and wherein the input layer or the hidden layer includes at least one passive phase modulator configured to locally modulate a phase of incident light depending on positions on a two dimensional plane.

An all-optical Diffractive Deep Neural Network (D.sup.2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D.sup.2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D.sup.2NNs. In alternative embodiments, the all-optical D.sup.2NN is used as a front-end in conjunction with a trained, digital neural network back-end.