Abstract: Improved training of optical neural networks is provided. In one example: 1) we choose input and target vectors, we program those into an input vector generator and a measurement unit, respectively, we turn on the optical input source power, and we monitor the electrical signal representing the cost function. 2) we can then modulate two or more controllable elements inside the optical network at different frequencies and look for the size and sign of the corresponding distinct AC variations in the measured cost function, simultaneously giving us the gradients with respect to each element.

Abstract: Systems and methods for activation in an optical circuit in accordance with embodiments of the invention are illustrated. One embodiment includes an optical activation circuit, wherein the circuit comprises a directional coupler, an optical-to-electrical conversion circuit, a time delay element, a nonlinear signal conditioner, and a phase shifter. The directional coupler receives an optical input and provides a first portion to the optical-to-electrical conversion circuit and a second portion to the time delay element, the time delay element provides a delayed signal to the phase shifter, and the optical-to-electrical conversion circuit converts an optical signal from the directional coupler to an electrical signal used to activate the phase shifter to shift the phase of the delayed signal.

Abstract: A method is disclosed for designing an analog computer that implements a trained recurrent neural network. A computer simulates a wave-based physical system including a wave propagation domain, a boundary layer that approximates a boundary condition, a source of waves, probes for measuring properties of propagated waves, a material within a central region of the wave propagation domain. The simulation also includes a discretized numerical model of a differential equation describing dynamics of wave propagation in the physical system.

Abstract: Systems and methods for training photonic neural networks in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a set of one or more optical interference units (OIUs) of a photonic artificial neural network (ANN), wherein the method includes calculating a loss for an original input to the photonic ANN, computing an adjoint input based on the calculated loss, measuring intensities for a set of one or more phase shifters in the set of OIUs when the computed adjoint input and the original input are interfered with each other within the set of OIUs, computing a gradient from the measured intensities, and tuning phase shifters of the OIU based on the computed gradient.

Abstract: Systems and methods for activation in an optical circuit in accordance with embodiments of the invention are illustrated. One embodiment includes an optical activation circuit, wherein the circuit comprises a directional coupler, an optical-to-electrical conversion circuit, a time delay element, a nonlinear signal conditioner, and a phase shifter. The directional coupler receives an optical input and provides a first portion to the optical-to-electrical conversion circuit and a second portion to the time delay element, the time delay element provides a delayed signal to the phase shifter, and the optical-to-electrical conversion circuit converts an optical signal from the directional coupler to an electrical signal used to activate the phase shifter to shift the phase of the delayed signal.