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    SELF-ORDERING FAST FOURIER TRANSFORM FOR SINGLE INSTRUCTION MULTIPLE DATA ENGINES
    25.
    发明公开
    SELF-ORDERING FAST FOURIER TRANSFORM FOR SINGLE INSTRUCTION MULTIPLE DATA ENGINES 审中-公开

    公开(公告)号：EP4307138A1

    公开(公告)日：2024-01-17

    申请号：EP23184654.4

    申请日：2023-07-11

    申请人： NXP USA, Inc.

    发明人： Mundarath, Jayakrishnan Cheriyath ,  Traylor, Kevin Bruce

    IPC分类号： G06F17/14 ,  G06F9/38

    摘要： A method for self-ordering Fast Fourier Transform for Single Instruction Multiple Data engines includes performing a butterfly operation on a first input vector and a second input vector to generate a first output vector and a second output vector, wherein the first input vector, the second input vector, the first output vector and the second output vector are each comprised of complex numbers, and a first order of the complex numbers of the first output vector is non-linear and a second order of the complex numbers of the second output vector is non-linear. A combination of complex numbers is reordered and exchanged between the first output vector and the second output vector to partially linearize the first order of the first output vector and to partially linearize the second order of the second output vector.

    PROCESSING UNIT, COMPUTING DEVICE AND INSTRUCTION PROCESSING METHOD
    29.
    发明公开
    PROCESSING UNIT, COMPUTING DEVICE AND INSTRUCTION PROCESSING METHOD 审中-公开

    公开(公告)号：EP4293502A1

    公开(公告)日：2023-12-20

    申请号：EP22217388.2

    申请日：2022-12-30

    申请人： T-Head (Shanghai) Semiconductor Co., Ltd.

    发明人： LIU, Dongqi ,  CHEN, Haowen ,  JIANG, Zhao ,  LIU, Chang ,  WEI, Dingyan ,  XU, Wenjian ,  JIANG, Tao

    IPC分类号： G06F9/30 ,  G06F9/38

    摘要： The embodiments of the present application provide a processing unit. The processing unit comprises: an instruction fetching unit configured for fusing instruction of vector configuration instruction and vector operation instruction that are adjacent in order to obtain fusion instruction; an instruction decoding unit configured to decode the fusion instruction to obtain first execution information and second execution information; a vector configuration unit configured to execute the vector configuration instruction according to the first execution information, modify vector control register, and bypass the value of the modified vector control register to the vector operation unit; the vector operation unit configured to execute the vector operation instruction according to the second execution information and the value of the modified vector control register.

    DETERMINING PROBLEM DEPENDENCIES IN APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL
    30.
    发明公开
    DETERMINING PROBLEM DEPENDENCIES IN APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL 审中-公开

    公开(公告)号：EP4242849A3

    公开(公告)日：2023-11-29

    申请号：EP23184633.8

    申请日：2020-06-26

    申请人： Capital One Services, LLC

    发明人： BALASUBRAMANIAN, Muralidharan ,  BARNUM, Eric K. ,  DALLEN, Julie ,  WATSON, David

    IPC分类号： G06F9/38 ,  G06F9/445 ,  G06F9/54 ,  G06F16/903 ,  G06N20/00 ,  G06F11/07 ,  G06F11/30

    摘要： Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.