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    ENERGY-AWARE TASK SCHEDULING
    4.
    发明公开
    ENERGY-AWARE TASK SCHEDULING 审中-公开

    公开(公告)号：US20230195531A1

    公开(公告)日：2023-06-22

    申请号：US17558624

    申请日：2021-12-22

    申请人： Intel Corporation

    发明人： Maruti GUPTA HYDE ,  Nageen HIMAYAT ,  Ravikumar BALAKRISHNAN ,  Mustafa AKDENIZ ,  Marcin SPOCZYNSKI ,  Arjun ANAND ,  Marius ARVINTE

    IPC分类号： G06F9/50 ,  G06F1/3203 ,  G06F9/48 ,  G06N3/02

    CPC分类号： G06F9/5055 ,  G06F9/5027 ,  G06F1/3203 ,  G06F9/4887 ,  G06N3/02

    摘要： A task modeling system, including a plurality of processing clients having a plurality of processing cores; a task modeler, including a memory storing an artificial neural network; and a processor, configured to receive input data representing a plurality of processing tasks to be completed by the processing client within a predefined time duration; and implement its artificial neural network to determine from the input data an assignment of the processing tasks among the processing cores for completion of the processing tasks within the predefined time duration, and determine a power management factor for each of the plurality of processing cores for power management during the predefined time duration; wherein the artificial neural network is configured to select the power management factor for each of the plurality of processing cores to achieve a power usage within a predefined threshold for the plurality of processing cores during the predefined time duration.

    OFFLOADED DATA PROCESSING FOR NAMED BASED DATA TRANSMISSIONS
    9.
    发明公开
    OFFLOADED DATA PROCESSING FOR NAMED BASED DATA TRANSMISSIONS 审中-公开

    公开(公告)号：US20230195538A1

    公开(公告)日：2023-06-22

    申请号：US18056266

    申请日：2022-11-17

    申请人： Intel Corporation

    发明人： Marcin SPOCZYNSKI ,  Hao FENG ,  Maruti GUPTA HYDE ,  Nageen HIMAYAT ,  Srikathyayani SRIKANTESWARA ,  Yi ZHANG

    IPC分类号： G06F9/54 ,  H04M1/72406

    CPC分类号： G06F9/54 ,  H04M1/72406

    摘要： A device may include a processor that includes a named data network (NDN) forwarding daemon (NFD) module. The NFD module may be configured to identify an accelerator on which to configure an offloaded NFD module based on a configuration table indicating a current configuration of the accelerator. The NFD module may be configured to configure the offloaded NFD module on the identified accelerator. The NFD module may be configured to receive an interest packet that includes a workload request. The interest packet may be configured according to an NDN protocol. The NFD module may be configured to determine that the offloaded NFD module is configured to perform the workload request using the identified accelerator based on the configuration table. The NFD module may be configured to offload, via an application programming interface, the workload request to the offloaded NFD module to perform the workload request using the identified accelerator.

    SYSTEM ENERGY EFFICIENCY IN A WIRELESS NETWORK
    10.
    发明公开
    SYSTEM ENERGY EFFICIENCY IN A WIRELESS NETWORK 审中-公开

    公开(公告)号：US20230188233A1

    公开(公告)日：2023-06-15

    申请号：US17549937

    申请日：2021-12-14

    申请人： Intel Corporation

    发明人： Maruti GUPTA HYDE ,  Vaibhav SINGH ,  Christian MACIOCCO ,  Nageen HIMAYAT ,  Rath VANNITHAMBY ,  Satish JHA

    IPC分类号： H04B17/391 ,  G06N3/02 ,  H04W24/06 ,  H04W48/16

    CPC分类号： H04B17/3912 ,  H04B17/3913 ,  G06N3/02 ,  H04W24/06 ,  H04W48/16

    摘要： The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained machine learning model, the input data representative of a network environment of the wireless network, wherein the trained machine learning model is configured to provide, based on the input data, output data representative of an expected performance of a plurality of configurations of network components with respect to power consumption and performance of the wireless network; select a configuration of a network component from the plurality of configurations based on the output data of the trained machine learning model; and instruct an operation of the network component according to the selected configuration; and a memory coupled with the processor, the memory storing the input data provided to the trained machine learning model and/or the output data from the trained machine learning model.