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    SYNCHRONOUS LOW-LATENCY MEDIA ACCESS CONTROL
    176.
    发明申请
    SYNCHRONOUS LOW-LATENCY MEDIA ACCESS CONTROL 审中-公开
    同步低功能媒体访问控制

    公开(公告)号：US20160270018A1

    公开(公告)日：2016-09-15

    申请号：US15066954

    申请日：2016-03-10

    Applicant: QUALCOMM Incorporated

    Inventor： Linhai HE ,  Rashid Ahmed Akbar ATTAR ,  Bibhu MOHANTY ,  Joseph Binamira SORIAGA ,  Maksim KRASNYANSKIY ,  Yacob COHEN-ARAZI ,  Rajesh KUMAR ,  Ron SCHNEIDER ,  Wenjia LAI

    IPC: H04W56/00 ,  H04W74/08 ,  H04W36/30 ,  H04L5/00 ,  H04W74/04 ,  H04W36/38

    CPC classification number: H04W56/0025 ,  H04L1/0003 ,  H04L1/0009 ,  H04L1/0025 ,  H04L5/0007 ,  H04L5/0048 ,  H04L5/0053 ,  H04L27/2655 ,  H04W36/0005 ,  H04W36/30 ,  H04W56/0015 ,  H04W72/1284 ,  H04W74/04 ,  H04W84/12

    Abstract: Various aspects are provided for low-latency wireless local area networks (WLANs). An access point (AP) may transmit a downlink pilot signal for synchronization of the AP with one or more wireless stations. The AP may receive an uplink control block synchronized with the downlink pilot signal including a reservation for uplink transmission from a first wireless station of the one or more wireless stations. The reservation may include an uplink pilot signal and a modulated pilot signal and indicate that the first wireless station has traffic for uplink transmission to the AP. The AP may schedule the first wireless station for uplink transmission during a traffic block after the uplink control block. The AP may estimate a wireless channel to the first wireless station based on the pilot signal and the modulated pilot signal. Other low-latency aspects apply to WLANs in which the AP and associated wireless stations are synchronized.

    Abstract translation: 为低延迟无线局域网（WLAN）提供了各种方面。 接入点（AP）可以发送用于AP与一个或多个无线站的同步的下行链路导频信号。 AP可以接收与下行链路导频信号同步的上行链路控制块，该上行链路控制块包括来自一个或多个无线站的第一无线站的上行链路传输的预留。 预约可以包括上行链路导频信号和调制的导频信号，并且指示第一无线站具有用于上行链路传输到AP的业务。 AP可以在上行链路控制块之后的业务块期间调度第一无线站用于上行链路传输。 AP可以基于导频信号和调制的导频信号估计到第一无线站的无线信道。 其他低延迟方面适用于AP和相关无线站同步的WLAN。

    ACCELERATING INFERENCING IN GENERATIVE ARTIFICIAL INTELLIGENCE MODELS
    177.
    发明申请
    ACCELERATING INFERENCING IN GENERATIVE ARTIFICIAL INTELLIGENCE MODELS 有权

    公开(公告)号：US20250021761A1

    公开(公告)日：2025-01-16

    申请号：US18545804

    申请日：2023-12-19

    Applicant: QUALCOMM Incorporated

    Inventor： Arvind Vardarajan SANTHANAM ,  Joseph Binamira SORIAGA ,  Roland MEMISEVIC ,  Mingu LEE ,  Christopher LOTT

    IPC: G06F40/284

    Abstract: Techniques and apparatus for generating a response to a query input into a generative artificial intelligence model. An example method generally includes generating, based on an input query and a first generative artificial intelligence model, a sequence of tokens corresponding to a candidate response to the input query. The sequence of tokens and the input query are output to a second generative artificial intelligence model for verification. One or more first guidance signals for the generated sequence of tokens are received from the second generative artificial intelligence model. The candidate response to the input query is revised based on the generated sequence of tokens and the one or more first guidance signals, and the revised candidate response is output as a response to the received input query.

    DISTRIBUTED MACHINE LEARNING COMPILER OPTIMIZATION
    178.
    发明公开
    DISTRIBUTED MACHINE LEARNING COMPILER OPTIMIZATION 审中-公开

    公开(公告)号：US20240177048A1

    公开(公告)日：2024-05-30

    申请号：US18056379

    申请日：2022-11-17

    Applicant: QUALCOMM Incorporated

    Inventor： Weiliang ZENG ,  Christopher G. LOTT ,  Edward H. TEAGUE ,  Yang YANG ,  Joseph Binamira SORIAGA

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: A method for optimizing the compilation of a machine learning model to be executed on target edge devices is provided. Compute nodes of a plurality of compute nodes are allocated to a compiler optimization process for a compiler of said machine learning model. The machine learning model has a compute graph representation having nodes that are kernel operators necessary to execute the machine learning model and edges that connect said kernel operators to define precedence constraints. A round of optimization is scheduled for the process amongst the allocated compute nodes. At each allocated compute node a sequencing and scheduling solution is applied per round to obtain a performance metric for the machine learning model. From each compute node the performance metric is received and a solution that has the best performance metric is identified and implemented for execution of the machine learning model on the target edge devices.

    SPARSITY-INDUCING FEDERATED MACHINE LEARNING
    180.
    发明公开
    SPARSITY-INDUCING FEDERATED MACHINE LEARNING 审中-公开

    公开(公告)号：US20230169350A1

    公开(公告)日：2023-06-01

    申请号：US18040111

    申请日：2021-09-28

    Applicant: QUALCOMM Incorporated

    Inventor： Christos LOUIZOS ,  Hossein HOSSEINI ,  Matthias REISSER ,  Max WELLING ,  Joseph Binamira SORIAGA

    IPC: G06N3/098

    CPC classification number: G06N3/098

    Abstract: Aspects described herein provide techniques for performing federated learning of a machine learning model, comprising: for each respective client of a plurality of clients and for each training round in a plurality of training rounds: generating a subset of model elements for the respective client based on sampling a gate probability distribution for each model element of a set of model elements for a global machine learning model; transmitting to the respective client: the subset of model elements; and a set of gate probabilities based on the sampling, wherein each gate probability of the set of gate probabilities is associated with one model element of the subset of model elements; receiving from each respective client of the plurality of clients a respective set of model updates; and updating the global machine learning model based on the respective set of model updates from each respective client of the plurality of clients.

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