REAL-TIME QUALITY OF SERVICE MONITORING APPARATUS AND METHOD

    公开(公告)号:US20170257287A1

    公开(公告)日:2017-09-07

    申请号:US15219549

    申请日:2016-07-26

    CPC classification number: H04L41/5038 H04L43/0817 H04L43/10

    Abstract: Provided herein is a real-time QoS monitoring apparatus, including an application registration unit configured to register at least one monitoring target application program for QoS measurement; a function explorer unit configured to detect user-defined functions in application code of the at least one monitoring target application program; a loop-statement explorer unit configured to detect loop-statements in the application code; a user-defined location explorer unit configured to detect user-defined locations in the application code; and a heartbeat generator configured to generate a plurality of heartbeat calls to correspond to the functions detected by the function finder, the loop-statements detected by the loop finder, and the user-defined locations detected by the user-defined location finder. Accordingly, there are provided a real-time QoS monitoring apparatus and method, which may measure QoS in real time without additionally modifying the application program.

    GPU-BASED ADAPTIVE BLAS OPERATION ACCELERATION APPARATUS AND METHOD THEREOF

    公开(公告)号:US20190228344A1

    公开(公告)日:2019-07-25

    申请号:US16013847

    申请日:2018-06-20

    Abstract: Disclosed herein are an apparatus and method for adaptively accelerating a BLAS operation based on a GPU. The apparatus for adaptively accelerating a BLAS operation based on a GPU includes a BLAS operation acceleration unit for setting optimal OpenCL parameters using machine-learning data attribute information and OpenCL device information and for creating a kernel in a binary format by compiling kernel source code; an OpenCL execution unit for creating an OpenCL buffer for a BLAS operation using information about an OpenCL execution environment and the optimal OpenCL parameters and for accelerating machine learning in an embedded system in such a way that a GPU that is capable of accessing the created OpenCL buffer performs the BLAS operation using the kernel, and an accelerator application unit for returning the result of the BLAS operation to a machine-learning algorithm.

    APPARATUS AND METHOD FOR MACHINE LEARNING BASED ON MONOTONICALLY INCREASING QUANTIZATION RESOLUTION

    公开(公告)号:US20210365838A1

    公开(公告)日:2021-11-25

    申请号:US17326238

    申请日:2021-05-20

    Abstract: Disclosed herein are an apparatus and method for machine learning based on monotonically increasing quantization resolution. The method, in which a quantization coefficient is defined as a monotonically increasing function of time, includes initially setting the monotonically increasing function of time, performing machine learning based on a quantized learning equation using the quantization coefficient defined by the monotonically increasing function of time, determining whether the quantization coefficient satisfies a predetermined condition after increasing the time, newly setting the monotonically increasing function of time when the quantization coefficient satisfies the predetermined condition, and updating the quantization coefficient using the newly set monotonically increasing function of time. Here, performing the machine learning, determining whether the quantization coefficient satisfies the predetermined condition, newly setting the monotonically increasing function of time, and updating the quantization coefficient may be repeatedly performed.

    DEVICE AND METHOD FOR CONFIGURING MONITORING ENVIRONMENT OF APPLICATION

    公开(公告)号:US20170255541A1

    公开(公告)日:2017-09-07

    申请号:US15206796

    申请日:2016-07-11

    Inventor: Jeong-Si KIM

    CPC classification number: G06F11/3495 G06F11/302 G06F11/3668

    Abstract: A method for configuring a monitoring environment of an application includes a monitoring location analysis step for detecting a monitoring location candidate, and a monitoring range analysis step for detecting a monitoring range, wherein the monitoring location analysis step includes receiving a source file of a general application, input m units of source lines, when an input source line is an execution line, calculating an execution load of the input source line, and determining a source line having the greatest execution load as a monitoring location candidate. Accordingly, it is possible to provide a method for determining an optimal performance monitoring location and an optimal performance monitoring range, which are required to configure an execution environment of a self-adaptive application, so as to perform efficient performance monitoring of the self-adaptive application.

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