Programmable Spatial Array for Matrix Decomposition

    公开(公告)号:US20230297538A1

    公开(公告)日:2023-09-21

    申请号:US18017077

    申请日:2020-09-25

    CPC classification number: G06F15/8092 G06F17/16

    Abstract: Programmable spatial array processing circuitry may be programmable to perform multiple different types of matrix decompositions. The programmable spatial array processing circuitry may include an array of processing elements. When programmed with a first instructions, the array performs a first type of matrix decomposition. When programmed with second instructions, the array performs a second type of matrix decomposition. Individual processing elements of the programmable spatial array processing circuitry may avoid having individual instruction memories. Instead, there may be an instruction memory that provides a portion of the first instructions or a portion of the second instructions sequentially to one processing element of a row of processing elements to sequentially propagate to other processing elements of the row of processing elements.

    Programmable spatial array for matrix decomposition

    公开(公告)号:US12235793B2

    公开(公告)日:2025-02-25

    申请号:US18017077

    申请日:2020-09-25

    Abstract: Programmable spatial array processing circuitry may be programmable to perform multiple different types of matrix decompositions. The programmable spatial array processing circuitry may include an array of processing elements. When programmed with a first instructions, the array performs a first type of matrix decomposition. When programmed with second instructions, the array performs a second type of matrix decomposition. Individual processing elements of the programmable spatial array processing circuitry may avoid having individual instruction memories. Instead, there may be an instruction memory that provides a portion of the first instructions or a portion of the second instructions sequentially to one processing element of a row of processing elements to sequentially propagate to other processing elements of the row of processing elements.

    Adaptive deep learning model for noisy image super-resolution

    公开(公告)号:US12033302B2

    公开(公告)日:2024-07-09

    申请号:US17435653

    申请日:2019-06-21

    Inventor: Wenyi Tang Xu Zhang

    CPC classification number: G06T3/4053 G06N3/047 G06T3/4046

    Abstract: Embodiments described herein are generally directed to an end-to-end trainable degradation restoration network (DRN) that enhances the ability of a super-resolution (SR) subnetwork to deal with noisy low-resolution images. An embodiment of a method includes estimating, by a noise estimator (NE) subnetwork of the DRN, an estimated noise map for a noisy input image; and predicting, by the SR subnetwork of the DRN, a clean upscaled image based on the input image and the noise map by, for each of multiple conditional residual dense blocks (CRDBs) stacked within one or more cascade blocks representing the SR subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a multiple residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the input image by applying (i) a scaling factor and (ii) an offset factor derived from the noise map.

    METHODS AND DEVICES FOR PROCESSING A DATA SIGNAL FOR TRANSMISSION TO MULTI-STREAM TERMINALS

    公开(公告)号:US20200091986A1

    公开(公告)日:2020-03-19

    申请号:US16494474

    申请日:2017-04-07

    Abstract: The disclosure relates to a radio transceiver, comprising: a precoder configured to precode a data signal for transmission to a plurality of multi-stream terminals based on a plurality of precoding weight matrices; and a processor configured to generate for each terminal in an iterative manner a precoding weight matrix and a transformed channel matrix, wherein the transformed channel matrix indicates a channel gain between the radio transceiver and the respective terminal transformed by a receive filter matrix of the respective terminal, wherein the generation of the precoding weight matrix and the transformed channel matrix in a current iteration is based on the transformed channel matrix generated from a previous iteration.

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