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    AUTOMATIC PIPELINE STAGE INSERTION
    41.
    发明申请
    AUTOMATIC PIPELINE STAGE INSERTION 有权
    自动管道插入

    公开(公告)号：US20140143531A1

    公开(公告)日：2014-05-22

    申请号：US13680399

    申请日：2012-11-19

    Applicant: QUALCOMM TECHNOLOGIES, INC. ,  ARTERIS SAS

    Inventor： Jean-Jacques Lecler

    IPC: G06F9/445

    CPC classification number: G06F9/445 ,  G06F9/3875 ,  G06F17/505 ,  G06F2217/84

    Abstract: The optimal configuration of a number of optional pipeline stages within the data paths of systems-on-chip is determined by application of a solver. The solver includes variables such as: the placement of modules physically within the floorplan of the chip; the signal propagation time; the logic gate switching time; the arrival time, after a clock edge, of a signal at each module port; the arrival time at each pipeline stage; and the Boolean value of the state of activation of each optional pipeline stage. The optimal configuration ensures that a timing constraint is met, if possible, with the lowest possible cost of pipeline stages.

    Abstract translation: 通过应用求解器确定片上系统的数据路径内的多个可选流水线级的最佳配置。 求解器包括以下变量：将模块物理放置在芯片的平面图中; 信号传播时间; 逻辑门切换时间; 每个模块端口的信号到达时间，时钟沿之后; 每个流水线阶段的到达时间; 以及每个可选流水线阶段的激活状态的布尔值。 最佳配置确保了在可能的情况下满足流水线阶段可能的最低成本的时序约束。

    Zone-based federated learning
    42.
    发明授权
    Zone-based federated learning 有权

    公开(公告)号：US12231302B2

    公开(公告)日：2025-02-18

    申请号：US18452492

    申请日：2023-08-18

    Applicant: QUALCOMM Technologies, Inc.

    Inventor： Vijaya Datta Mayyuri ,  An Chen

    IPC: H04L41/16 ,  G06F18/214 ,  H04W8/18 ,  H04W8/20 ,  H04W8/24

    Abstract: A method for managing model updates by a first zone server, associated with a first zone model of a plurality of zone models, includes receiving a global model from a global server associated with the global model. The method also includes transmitting the global model to user equipment (UEs) in a first group of UEs associated with the first zone model. The method further includes receiving, from one or more UEs in the first group, model updates associated with the global model based on transmitting the global model. The method further includes transmitting, to the global server, an average of the model updates received from the one or more UEs. The method also includes updating the global model to generate the first zone model based on the model updates. The method further includes transmitting the first zone model to one or more UEs in the first group.

    EFFICIENT NEURAL CAUSAL DISCOVERY
    45.
    发明公开
    EFFICIENT NEURAL CAUSAL DISCOVERY 审中-公开

    公开(公告)号：US20240176994A1

    公开(公告)日：2024-05-30

    申请号：US18551844

    申请日：2021-07-26

    Applicant: QUALCOMM TECHNOLOGIES, INC.

    Inventor： Phillip LIPPE ,  Taco Sebastiaan COHEN ,  Efstratios GAVVES

    IPC: G06N3/0464 ,  G06N3/09

    CPC classification number: G06N3/0464 ,  G06N3/09

    Abstract: A method for generating a causal graph includes receiving a data set including observation data and intervention data corresponding to multiple variables. A probability distribution is determined for each variable based on the observation data. A likelihood of including each edge in the graph is computed based on the probability distribution and the intervention data. Each edge is a causal connection between variables of the multiple variables. The graph is generated based on the likelihood of including each edge. The graph may be updated by iteratively repeating the determination of the probability distribution and the computing of the likelihood of including each edge.

    METHOD OF DETERMINING ZONE MEMBERSHIP IN ZONE-BASED FEDERATED LEARNING
    49.
    发明公开
    METHOD OF DETERMINING ZONE MEMBERSHIP IN ZONE-BASED FEDERATED LEARNING 审中-公开

    公开(公告)号：US20230385651A1

    公开(公告)日：2023-11-30

    申请号：US18102601

    申请日：2023-01-27

    Applicant: QUALCOMM Technologies, Inc.

    Inventor： Vijaya Datta MAYYURI ,  An CHEN

    IPC: G06N3/098 ,  G06N3/0464

    CPC classification number: G06N3/098 ,  G06N3/0464

    Abstract: A processor-implemented method includes receiving, by a user equipment (UE), a zone determination function based on registering for a federated learning process for training a first federated learning model. The method also includes determining, by the UE, a zone membership in accordance with UE parameters and the zone determination function. The method further includes selecting the first federated learning model, by the UE, based on the zone membership. The method includes training the first federated learning model by the UE.

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