公开(公告)号：US11373972B2

公开(公告)日：2022-06-28

申请号：US16902887

申请日：2020-06-16

Applicant: Intel Corporation

Inventor： Omkar G. Karhade ,  Nitin A. Deshpande ,  Mohit Bhatia ,  Anurag Tripathi ,  Takeshi Nakazawa ,  Steve Cho

IPC: H01L23/538 ,  H01L23/00

Abstract: Disclosed herein are microelectronic structures including bridges, as well as related assemblies and methods. In some embodiments, a microelectronic structure may include a substrate and a bridge.

公开(公告)号：US20220270998A1

公开(公告)日：2022-08-25

申请号：US17740501

申请日：2022-05-10

Applicant: Intel Corporation

Inventor： Omkar G. Karhade ,  Nitin A. Deshpande ,  Mohit Bhatia ,  Anurag Tripathi ,  Takeshi Nakazawa ,  Steve Cho

IPC: H01L23/00 ,  H01L23/538

Abstract: Disclosed herein are microelectronic structures including bridges, as well as related assemblies and methods. In some embodiments, a microelectronic structure may include a substrate and a bridge.

公开(公告)号：US11735558B2

公开(公告)日：2023-08-22

申请号：US17740501

申请日：2022-05-10

Applicant: Intel Corporation

Inventor： Omkar G. Karhade ,  Nitin A. Deshpande ,  Mohit Bhatia ,  Anurag Tripathi ,  Takeshi Nakazawa ,  Steve Cho

IPC: H01L23/538 ,  H01L23/00

CPC classification number: H01L24/30 ,  H01L23/5384 ,  H01L24/17 ,  H01L2224/1703

Abstract: Disclosed herein are microelectronic structures including bridges, as well as related assemblies and methods. In some embodiments, a microelectronic structure may include a substrate and a bridge.

公开(公告)号：US20210391294A1

公开(公告)日：2021-12-16

申请号：US16902887

申请日：2020-06-16

Applicant: Intel Corporation

Inventor： Omkar G. Karhade ,  Nitin A. Deshpande ,  Mohit Bhatia ,  Anurag Tripathi ,  Takeshi Nakazawa ,  Steve Cho

IPC: H01L23/00 ,  H01L23/538

Abstract: Disclosed herein are microelectronic structures including bridges, as well as related assemblies and methods. In some embodiments, a microelectronic structure may include a substrate and a bridge.

公开(公告)号：US11501153B2

公开(公告)日：2022-11-15

申请号：US15856755

申请日：2017-12-28

Applicant: Intel Corporation

Inventor： LayWai Kong ,  Takeshi Nakazawa ,  Anne Hansen-Musakwa

IPC: G06N3/08 ,  G06K9/62 ,  G06N7/00 ,  G06F16/903 ,  G06N3/04 ,  G06F17/11 ,  G06N5/00

Abstract: Methods, apparatus, systems, and articles of manufacture for training a neural network are disclosed. An example apparatus includes a training data segmenter to generate a partial set of labeled training data from a set of labeled training data. A matrix constructor is to create a design of experiments matrix identifying permutations of hyperparameters to be tested. A training controller is to cause a neural network trainer to train a neural network using a plurality of the permutations of hyperparameters in the design of experiments matrix and the partial set of labeled training data, and access results of the training corresponding of each of the permutations of hyperparameters. A result comparator is to select a permutation of hyperparameters based on the results, the training controller to instruct the neural network trainer to train the neural network using the selected permutation of hyperparameters and the labeled training data.

公开(公告)号：US20190050723A1

公开(公告)日：2019-02-14

申请号：US15856755

申请日：2017-12-28

Applicant: Intel Corporation

Inventor： LayWai Kong ,  Takeshi Nakazawa ,  Anne Hansen-Musakwa

IPC: G06N3/08 ,  G06K9/62 ,  G06N7/00 ,  G06F17/30

Abstract: Methods, apparatus, systems, and articles of manufacture for training a neural network are disclosed. An example apparatus includes a training data segmenter to generate a partial set of labeled training data from a set of labeled training data. A matrix constructor is to create a design of experiments matrix identifying permutations of hyperparameters to be tested. A training controller is to cause a neural network trainer to train a neural network using a plurality of the permutations of hyperparameters in the design of experiments matrix and the partial set of labeled training data, and access results of the training corresponding of each of the permutations of hyperparameters. A result comparator is to select a permutation of hyperparameters based on the results, the training controller to instruct the neural network trainer to train the neural network using the selected permutation of hyperparameters and the labeled training data.