公开(公告)号：US11763140B2

公开(公告)日：2023-09-19

申请号：US17394671

申请日：2021-08-05

Applicant: Intel Corporation

Inventor： Tomer Schwartz ,  Ehud Cohen ,  Uzi Sarel ,  Amitai Armon ,  Yaniv Fais ,  Lev Faivishevsky ,  Amit Bleiweiss ,  Yahav Shadmiy ,  Jacob Subag

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

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

Abstract: A mechanism is described for facilitating memory handling and data management in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting multiple tables associated with multiple neural networks at multiple autonomous machines, where each of the multiple tables include an index. The method may further include combining the multiple tables and multiple indexes associated with the multiple tables into a single table and a single index, respectively, where the single table is communicated to the multiple autonomous machines to allow simultaneous processing of one or more portions of the single table using one or more memory devices and one or more processors of one or more of the multiple autonomous machines.

公开(公告)号：US11093822B2

公开(公告)日：2021-08-17

申请号：US15499896

申请日：2017-04-28

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 ,  G06N3/04 ,  G06N3/08

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：US20180307982A1

公开(公告)日：2018-10-25

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 ,  G06N99/00

CPC classification number: G06N3/08 ,  G06N99/005 ,  G06T1/20

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：US12131507B2

公开(公告)日：2024-10-29

申请号：US18191565

申请日：2023-03-28

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 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N3/084 ,  G06N3/088 ,  H04N19/42 ,  H04N19/436

CPC classification number: G06T9/002 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N3/084 ,  G06N3/088 ,  H04N19/42 ,  H04N19/436

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. Other embodiments are also disclosed and claimed.

公开(公告)号：US20230316589A1

公开(公告)日：2023-10-05

申请号：US18191565

申请日：2023-03-28

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 ,  H04N19/436 ,  G06N3/084 ,  G06N3/088 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048

CPC classification number: G06T9/002 ,  H04N19/42 ,  H04N19/436 ,  G06N3/084 ,  G06N3/088 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048

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. Other embodiments are also disclosed and claimed.

公开(公告)号：US11600035B2

公开(公告)日：2023-03-07

申请号：US17669126

申请日：2022-02-10

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

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：US11238338B2

公开(公告)日：2022-02-01

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 ,  Raanan Yonatan Yehezkel Rohekar ,  Michael Behar ,  Amital Armon ,  Uzi Sarel

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：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.