LIRIC Diffractive Deep Neural Network
    1.
    发明公开

    公开(公告)号:US20240311628A1

    公开(公告)日:2024-09-19

    申请号:US18604226

    申请日:2024-03-13

    IPC分类号: G06N3/067 G06N3/049

    CPC分类号: G06N3/067 G06N3/049

    摘要: Optical elements for diffractive deep neural networks include one or more subsurface layers of diffractive optical elements. An optical element for a diffractive deep neural network includes a substrate and one or more subsurface layers of diffractive optical elements formed within the substrate. The substrate is made from an optical material having a base refractive index. Each of the one or more subsurface layers includes a respective subset of the diffractive optical elements. Each of the diffractive optical elements is formed within a respective sub-volume of the respective subsurface layer via induced changes in refractive index of the optical material to configure the diffractive optical element to function as a neuron in the diffractive deep neural network.

    DUAL ADAPTIVE TRAINING METHOD OF PHOTONIC NEURAL NETWORKS AND ASSOCIATED COMPONENTS

    公开(公告)号:US20240232617A1

    公开(公告)日:2024-07-11

    申请号:US18394052

    申请日:2023-12-22

    IPC分类号: G06N3/08 G06N3/067

    CPC分类号: G06N3/08 G06N3/067

    摘要: A dual adaptive training method of photonic neural networks (PNN), includes constructing, in a computer, a PNN numerical model including a PNN physical model and a systematic error prediction network model, where the PNN physical model is an error-free ideal PNN physical model of a PNN physical system, the systematic error prediction network model is an error model of the PNN physical system; determining measurement values of the PNN physical system and measurement values of the PNN numerical model, where the measurement values of the PNN physical system include final output values of the PNN physical system, and the measurement values of the PNN numerical model include final output values of the PNN numerical model; determining a similarity loss function based on comparison results between the measurement values of the PNN physical system and the measurement values of the PNN numerical model; determining a task loss function based on fused results of the measurement values of the PNN physical system and the measurement values of the PNN numerical model; and optimizing and updating parameters of the PNN numerical model based on the similarity loss function and the task loss function for in situ training of the PNN physical model.

    METHODS AND SYSTEMS TO OPTICALLY REALIZE NEURAL NETWORKS

    公开(公告)号:US20240185051A1

    公开(公告)日:2024-06-06

    申请号:US18441649

    申请日:2024-02-14

    IPC分类号: G06N3/067

    CPC分类号: G06N3/067

    摘要: Layers of a neural network can be implemented on an analog computing platform operative to perform MAC operations in series or in parallel in order to cover all elements of an arbitrary size matrix. Embodiments include a convolutional layer, a fully-connected layer, a batch normalization layer, a max pooling layer, an average pooling layer, a ReLU function layer, a sigmoid function layer, as well as concatenations and combinations. Applications include point cloud processing, and in particular of the processing of point clouds obtained as part of a LiDAR application. To implement elements of point cloud data as optical intensities, the data can be linearly translated in order to be fully represented with non-negative values.

    LIGHT DIFFRACTION ELEMENT UNIT AND OPTICAL COMPUTATION DEVICE

    公开(公告)号:US20240085600A1

    公开(公告)日:2024-03-14

    申请号:US18274557

    申请日:2022-01-14

    申请人: FUJIKURA LTD.

    IPC分类号: G02B5/18 G06N3/067

    CPC分类号: G02B5/18 G06N3/067

    摘要: A light diffraction element unit includes a base material including a light-transmissive and flexible layer member, a light diffraction structure including microcells and disposed on a portion of a main surface of the base material, and a holding part holding the base material and including a layer member or a plate member having an opening that penetrates through a pair of main surfaces of the layer member or the plate member. The holding part holds an annular portion of the base material such that the light diffraction structure is encompassed in the opening. The annular portion surrounds the portion of the main surface of the base material.

    Hadamard product implementation method and device, and storage medium

    公开(公告)号:US11893479B1

    公开(公告)日:2024-02-06

    申请号:US18267431

    申请日:2021-11-30

    IPC分类号: G06N3/067

    CPC分类号: G06N3/067

    摘要: A method for realizing a Hadamard product, a device and a storage medium, includes: acquiring a plurality of to-be-treated optical signals with unequal wavelengths; inputting the to-be-treated optical signals into a wavelength division multiplexer; by using the wavelength division multiplexer, feeding the to-be-treated optical signals to a micro-ring-resonator component, wherein the micro-ring-resonator component includes a plurality of micro-ring-resonator groups each of which is formed by two micro-ring resonators with equal radii; and applying a corresponding electric current to the micro-ring-resonator component, to obtain a result of the Hadamard product according to an outputted light intensity.

    SEMICONDUCTOR DEVICES OF OPTICAL NEURAL NETWORK AND METHODS OF FORMING THE SAME

    公开(公告)号:US20230409894A1

    公开(公告)日:2023-12-21

    申请号:US17844192

    申请日:2022-06-20

    IPC分类号: G06N3/067

    CPC分类号: G06N3/067

    摘要: A semiconductor device includes an oxide layer having a first side and a second side opposite to each other. The semiconductor device includes a plurality of first waveguides that are disposed across a plurality of first insulator layers, respectively, on the first side of the oxide layer. The semiconductor device includes a plurality of second waveguides that are disposed across a plurality of second insulator layers, respectively, on the second side of the oxide layer. The plurality of first waveguides and the plurality of second waveguides collectively form a plurality of photonic neural network layers of an artificial neural network.