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    NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING
    1.
    发明公开
    NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING 审中-公开

    公开(公告)号：US20240119301A1

    公开(公告)日：2024-04-11

    申请号：US18464996

    申请日：2023-09-11

    Applicant: QUALCOMM Incorporated

    Inventor： Wonseok JEON ,  Mukul GAGRANI ,  Weiliang ZENG ,  Edward TEAGUE ,  Burak BARTAN ,  Piero ZAPPI ,  Christopher LOTT

    IPC: G06N3/092

    CPC classification number: G06N3/092

    Abstract: A processor-implemented method includes sampling, according to a priority sampling policy, a set of node priorities from a computation graph. Each node priority of the set of node priorities may be associated with a respective node on the computation graph. Additionally, each node may represent an operation of a task performed by an artificial neural network. The method also includes converting, via a list scheduling function, the node priorities to a schedule that associates each node of the computation graph with a processor of a group of processors of a device associated with the artificial neural network, the schedule associated with a makespan. The method further includes performing the task in accordance with the schedule.

    MULTI-RESOLUTION FIELD REPRESENTATIONS IN NEURAL NETWORKS
    2.
    发明申请
    MULTI-RESOLUTION FIELD REPRESENTATIONS IN NEURAL NETWORKS 有权

    公开(公告)号：US20250094780A1

    公开(公告)日：2025-03-20

    申请号：US18468203

    申请日：2023-09-15

    Applicant: QUALCOMM Incorporated

    Inventor： Kartikeya BHARDWAJ ,  Piero ZAPPI ,  Paul Nicholas WHATMOUGH ,  Christopher LOTT ,  Viswanath GANAPATHY ,  Chirag Sureshbhai PATEL ,  Joseph Binamira SORIAGA

    IPC: G06N3/0464

    Abstract: Certain aspects provide techniques and apparatuses for efficiently processing inputs in a neural network using multiple receptive field sizes. An example method includes partitioning a first input into a first set of channels and a second set of channels. At a first layer of a neural network, the first set of channels and the second set of channels are convolved into a first output having a smaller dimensionality a dimensionality of the first input. The first set of channels and the first output are concatenated into a second input. The second input is convolved into a second output via a second layer of the neural network, wherein the second output merges a first receptive field generated by the first layer with a larger second receptive field generated by the second layer. One or more actions are taken based on at least one of the first output and the second output.

    DEPTH-FIRST DEEP CONVOLUTIONAL NEURAL NETWORK INFERENCE
    7.
    发明申请
    DEPTH-FIRST DEEP CONVOLUTIONAL NEURAL NETWORK INFERENCE 有权

    公开(公告)号：US20210182684A1

    公开(公告)日：2021-06-17

    申请号：US17121499

    申请日：2020-12-14

    Applicant: QUALCOMM Incorporated

    Inventor： Piero ZAPPI ,  Jin Won LEE ,  Christopher LOTT ,  Rexford Alan HILL

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method performed by a computing device includes determining a partition for depth-first processing by a multi-layer artificial neural network (ANN) of the computing device. The computing device comprising a processor, on-chip memory, and off-chip memory. The first partition determined based on an amount of on-chip memory used by the first partition, an available amount of on-chip memory, and a size of a write back to the off-chip memory. The method also includes processing, at the device via the multi-layer ANN, an input, using the depth-first processing in accordance with the partition.

    AUTO-CALIBRATING LIGHT SENSOR DATA OF A MOBILE DEVICE
    8.
    发明申请
    AUTO-CALIBRATING LIGHT SENSOR DATA OF A MOBILE DEVICE 审中-公开
    自动校准光传感器数据的移动设备

    公开(公告)号：US20170059401A1

    公开(公告)日：2017-03-02

    申请号：US14843790

    申请日：2015-09-02

    Applicant: QUALCOMM Incorporated

    Inventor： Santiago MAZUELAS ,  Ashwin SWAMINATHAN ,  Piero ZAPPI ,  Muralidhar Reddy AKULA ,  Abhijeet BISAIN ,  Aditya Narain SRIVASTAVA ,  Suhas Hariharapura SHESHADRI

    IPC: G01J1/44 ,  G01J1/42

    CPC classification number: G01J1/44 ,  G01J1/4204 ,  G01J2001/444 ,  G06F9/4405 ,  H04M1/22 ,  H04M1/72569 ,  H04M2250/12

    Abstract: A method of auto-calibrating light sensor data of a mobile device includes, obtaining, by the mobile device, one or more reference parameters representative of light sensor data collected by a reference device. The method also includes collecting, by the mobile device, light sensor data from a light sensor included in the mobile device, itself. One or more sample parameters of the light sensor data obtained from the light sensor included in the mobile device are then calculated. A calibration model is then determined for auto-calibrating the light sensor data of the light sensor included in the mobile device based on the one or more reference parameters and the one or more sample parameters.

    Abstract translation: 自动校准移动设备的光传感器数据的方法包括：由移动设备获得表示由参考设备收集的光传感器数据的一个或多个参考参数。 该方法还包括由移动设备从包括在移动设备中的光传感器本身收集光传感器数据。 然后计算从包括在移动设备中的光传感器获得的光传感器数据的一个或多个采样参数。 然后，基于一个或多个参考参数和一个或多个样本参数，确定校准模型，用于自动校准包括在移动设备中的光传感器的光传感器数据。

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