公开(公告)号：US11100393B2

公开(公告)日：2021-08-24

申请号：US15494887

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Lev Faivishevsky ,  Tomer Bar-On ,  Yaniv Fais ,  Jacob Subag ,  Jeremie Dreyfuss ,  Amit Bleiweiss ,  Tomer Schwartz

IPC: G06N3/08 ,  G06N20/00 ,  G06N20/10

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.

公开(公告)号：US10762685B2

公开(公告)日：2020-09-01

申请号：US16670749

申请日：2019-10-31

Applicant: Intel Corporation

Inventor： Uzi Sarel ,  Ehud Cohen ,  Tomer Schwartz ,  Amitai Armon ,  Yahav Shadmiy ,  Itamar Ben-Ari ,  Amit Bleiweiss ,  Lev Faivishevsky ,  Tomer Bar-On ,  Yaniv Fais ,  Jacob Subag ,  Michael Behar ,  Guy Jacob ,  Gal Leibovich ,  Jeremie Dreyfuss

IPC: G06N3/02 ,  G06N3/08 ,  G06N3/063 ,  G06T15/00 ,  G06N3/04

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

公开(公告)号：US20200143579A1

公开(公告)日：2020-05-07

申请号：US16670749

申请日：2019-10-31

Applicant: Intel Corporation

Inventor： Uzi Sarel ,  Ehud Cohen ,  Tomer Schwartz ,  Amitai Armon ,  Yahav Shadmiy ,  Itamar Ben-Ari ,  Amit Bleiweiss ,  Lev Faivishevsky ,  Tomer Bar-On ,  Yaniv Fais ,  Jacob Subag ,  Michael Behar ,  Guy Jacob ,  Gal Leibovich ,  Jeremie Dreyfuss

IPC: G06T15/00 ,  G06N3/02 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

公开(公告)号：US20180293777A1

公开(公告)日：2018-10-11

申请号：US15482724

申请日：2017-04-08

Applicant: Intel Corporation

Inventor： Uzi Sarel ,  Ehud Cohen ,  Tomer Schwartz ,  Amitai Armon ,  Yahav Shadmiy ,  Itamar Ben-Ari ,  Amit Bleiweiss ,  Lev Faivishevsky ,  Tomer Bar-On ,  Yaniv Fais ,  Jacob Subag ,  Michael Behar ,  Guy Jacob ,  Gal Leibovich ,  Jeremie Dreyfuss

IPC: G06T15/00 ,  G06N3/04 ,  G06N3/08

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

公开(公告)号：US12033063B2

公开(公告)日：2024-07-09

申请号：US18174275

申请日：2023-02-24

Applicant: Intel Corporation

Inventor： Eran Ben-Avi ,  Neta Zmora ,  Guy Jacob ,  Lev Faivishevsky ,  Jeremie Dreyfuss ,  Tomer Bar-On ,  Jacob Subag ,  Yaniv Fais ,  Shira Hirsch ,  Orly Weisel ,  Zigi Walter ,  Yarden Oren

IPC: G06N3/06 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084

CPC classification number: G06N3/063 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

公开(公告)号：US20240160910A1

公开(公告)日：2024-05-16

申请号：US18527734

申请日：2023-12-04

Applicant: Intel Corporation

Inventor： Uzi Sarel ,  Ehud Cohen ,  Tomer Schwartz ,  Amitai Armon ,  Yahav Shadmiy ,  Amit Bleiweiss ,  Gal Leibovich ,  Jeremie Dreyfuss ,  Lev Faivishevsky ,  Tomer Bar-On ,  Yaniv Fais ,  Jacob Subag

IPC: G06N3/063 ,  G06F9/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084

CPC classification number: G06N3/063 ,  G06F9/30014 ,  G06F9/30025 ,  G06F9/30043 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.

公开(公告)号：US20230281435A1

公开(公告)日：2023-09-07

申请号：US18174275

申请日：2023-02-24

Applicant: Intel Corporation

Inventor： Eran Ben-Avi ,  Neta Zmora ,  Guy Jacob ,  Lev Faivishevsky ,  Jeremie Dreyfuss ,  Tomer Bar-On ,  Jacob Subag ,  Yaniv Fais ,  Shira Hirsch ,  Orly Weisel ,  Zigi Walter ,  Yarden Oren

IPC: G06N3/063 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/063 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

公开(公告)号：US11704564B2

公开(公告)日：2023-07-18

申请号：US17404153

申请日：2021-08-17

Applicant: Intel Corporation

Inventor： Lev Faivishevsky ,  Tomer Bar-On ,  Yaniv Fais ,  Jacob Subag ,  Jeremie Dreyfuss ,  Amit Bleiweiss ,  Tomer Schwartz ,  Raanan Yonatan Yehezkel Rohekar ,  Michael Behar ,  Amitai Armon ,  Uzi Sarel

IPC: G06N3/08 ,  G06N20/00 ,  G06N20/10

CPC classification number: G06N3/08 ,  G06N20/00 ,  G06N20/10

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.

公开(公告)号：US11620766B2

公开(公告)日：2023-04-04

申请号：US17344639

申请日：2021-06-10

Applicant: INTEL CORPORATION

Inventor： Tomer Bar-On ,  Jacob Subag ,  Yaniv Fais ,  Jeremie Dreyfuss ,  Gal Novik ,  Gal Leibovich ,  Tomer Schwartz ,  Ehud Cohen ,  Lev Faivishevsky ,  Uzi Sarel ,  Amitai Armon ,  Yahav Shadmiy

IPC: G06T9/00 ,  H04N19/42 ,  G06N3/04 ,  H04N19/436 ,  G06N3/084 ,  G06N3/088

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer.

公开(公告)号：US11599777B2

公开(公告)日：2023-03-07

申请号：US15499900

申请日：2017-04-28

Applicant: Intel Corporation

Inventor： Eran Ben-Avi ,  Neta Zmora ,  Guy Jacob ,  Lev Faivishevsky ,  Jeremie Dreyfuss ,  Tomer Bar-On ,  Jacob Subag ,  Yaniv Fais ,  Shira Hirsch ,  Orly Weisel ,  Zigi Walter ,  Yarden Oren

IPC: G06N3/00 ,  G06N3/063 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.