公开(公告)号：US20190050265A1

公开(公告)日：2019-02-14

申请号：US16146845

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Divya Vijayaraghavan ,  Denica Larsen ,  Kooi Chi Ooi ,  Lady Nataly Pinilla Pico ,  Min Suet Lim

IPC: G06F9/50 ,  G06N3/08 ,  G06K9/62 ,  G06F11/30 ,  G06N3/04

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

公开(公告)号：US11030012B2

公开(公告)日：2021-06-08

申请号：US16146845

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Divya Vijayaraghavan ,  Denica Larsen ,  Kooi Chi Ooi ,  Lady Nataly Pinilla Pico ,  Min Suet Lim

IPC: G06F9/50 ,  G06N3/08 ,  G06N3/04 ,  G06F11/30 ,  G06K9/62

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

公开(公告)号：US20190050715A1

公开(公告)日：2019-02-14

申请号：US16147037

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Kooi Chi Ooi ,  Min Suet Lim ,  Denica Larsen ,  Lady Nataly Pinilla Pico ,  Divya Vijayaraghavan

IPC: G06N3/04 ,  G06K9/62 ,  G06N5/04 ,  G06N3/08 ,  G06F15/78 ,  G06F1/16 ,  G06F17/30 ,  G06N99/00

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve data training of a machine learning model using a field-programmable gate array (FPGA). An example system includes one or more computation modules, each of the one or more computation modules associated with a corresponding user, the one or more computation modules training first neural networks using data associated with the corresponding users, and FPGA to obtain a first set of parameters from each of the one or more computation modules, the first set of parameters associated with the first neural networks, configure a second neural network based on the first set of parameters, execute the second neural network to generate a second set of parameters, and transmit the second set of parameters to the first neural networks to update the first neural networks.

公开(公告)号：US11710029B2

公开(公告)日：2023-07-25

申请号：US16147037

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Kooi Chi Ooi ,  Min Suet Lim ,  Denica Larsen ,  Lady Nataly Pinilla Pico ,  Divya Vijayaraghavan

IPC: G06N3/045 ,  G06N3/08 ,  G06N5/04 ,  G06N3/063 ,  G06F15/78 ,  G06F1/16 ,  G06N20/00 ,  G06F16/00 ,  G06N3/084 ,  G06V10/94 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2413 ,  G06N3/048 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06N3/063 ,  G06F1/163 ,  G06F15/7892 ,  G06F16/00 ,  G06F18/214 ,  G06F18/217 ,  G06F18/24143 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06N3/084 ,  G06N5/04 ,  G06N20/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V10/955

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve data training of a machine learning model using a field-programmable gate array (FPGA). An example system includes one or more computation modules, each of the one or more computation modules associated with a corresponding user, the one or more computation modules training first neural networks using data associated with the corresponding users, and FPGA to obtain a first set of parameters from each of the one or more computation modules, the first set of parameters associated with the first neural networks, configure a second neural network based on the first set of parameters, execute the second neural network to generate a second set of parameters, and transmit the second set of parameters to the first neural networks to update the first neural networks.

公开(公告)号：US20210406085A1

公开(公告)日：2021-12-30

申请号：US17317679

申请日：2021-05-11

Applicant: Intel Corporation

Inventor： Divya Vijayaraghavan ,  Denica Larsen ,  Kooi Chi Ooi ,  Lady Nataly Pinilla Pico ,  Min Suet Lim

IPC: G06F9/50 ,  G06N3/08 ,  G06N3/04 ,  G06F11/30 ,  G06K9/62

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

公开(公告)号：US12074368B2

公开(公告)日：2024-08-27

申请号：US17754149

申请日：2019-12-27

Applicant: INTEL CORPORATION

Inventor： Denica Larsen ,  Dong-Ho Han ,  Kwan Ho Lee ,  Shantanu Kulkarni ,  Jaejin Lee

IPC: H01Q1/52 ,  H01Q1/24 ,  H01Q9/28 ,  H01Q9/42

CPC classification number: H01Q1/526 ,  H01Q1/243 ,  H01Q9/285 ,  H01Q9/42

Abstract: An electronic computing device with a self-shielding antenna. An electronic computing device may include a frame, an antenna, and an antenna shielding. The frame includes a top cover and a bottom cover. Electronic components are included in a space formed between the top cover and the bottom cover. The antenna is for wireless transmission and reception and included in the frame near an edge of the frame. The antenna shielding is disposed around the antenna for providing electro-magnetic shielding from radio frequency (RE) noises generated from the electronic components included in the frame. The antenna shielding may be a metal wall disposed between the top cover and the bottom cover around the antenna. The frame may be a metallic frame and may include a cut-out in the top cover and the bottom cover above and below the antenna, and a non-metallic cover may be provided in the cut-out.

公开(公告)号：US12036467B2

公开(公告)日：2024-07-16

申请号：US17314167

申请日：2021-05-07

Applicant: Intel Corporation

Inventor： Duck Young Kong ,  Denica Larsen ,  Shantanu Kulkarni

IPC: A63F13/24 ,  A63F13/92

CPC classification number: A63F13/24 ,  A63F13/92

Abstract: Examples relate to a handheld device, a dock for a handheld device, and to corresponding methods and systems. The handheld device comprises a main unit comprising a display of the handheld device. The handheld device comprises two input controllers being non-removably attached to the main unit via an extension mechanism. The extension mechanism is configured such, that the two input controllers are movable from a retracted configuration to an extended configuration.

公开(公告)号：US11586473B2

公开(公告)日：2023-02-21

申请号：US17317679

申请日：2021-05-11

Applicant: Intel Corporation

Inventor： Divya Vijayaraghavan ,  Denica Larsen ,  Kooi Chi Ooi ,  Lady Nataly Pinilla Pico ,  Min Suet Lim

IPC: G06F9/50 ,  G06N3/08 ,  G06N3/04 ,  G06F11/30 ,  G06K9/62

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.