公开(公告)号：US20200326696A1

公开(公告)日：2020-10-15

申请号：US16913845

申请日：2020-06-26

Applicant: Intel Corporation

Inventor： Ignacio Alvarez ,  Todd Anderson ,  Vy Vo ,  Javier Felip Leon ,  Javier Perez-Ramirez

IPC: G05B23/02 ,  G05B13/02

Abstract: Systems, apparatuses and methods may provide for technology that obtains categorization information and corresponding uncertainty information from a perception subsystem, wherein the categorization information and the corresponding uncertainty information are to be associated with an object in an environment. The technology may also determine whether the corresponding uncertainty information satisfies one or more relevance criteria, and automatically control the perception subsystem to increase an accuracy in one or more subsequent categorizations of the object if the corresponding uncertainty information satisfies the one or more relevance criteria. In one example, determining whether the corresponding uncertainty information satisfies the relevance criteria includes taking a plurality of samples from the categorization information and the corresponding uncertainty information, generating a plurality of actuation plans based on the plurality of samples, and determining a safety deviation across the plurality of actuation plans, wherein the relevance criteria are satisfied if the safety deviation exceeds a threshold.

公开(公告)号：US20190325314A1

公开(公告)日：2019-10-24

申请号：US16456863

申请日：2019-06-28

Applicant: Intel Corporation

Inventor： Mikael Bourges-Sevenier ,  Adam Herr ,  Sridhar Sharma ,  Derek Gerstmann ,  Todd Anderson ,  Justin Gottschlich

IPC: G06N3/08 ,  G06F17/16

Abstract: Methods, apparatus, systems and articles of manufacture to optimize execution of a machine learning model are disclosed. An example apparatus includes a quantizer to quantize a layer of a model based on an execution constraint, the layer of the model represented by a matrix. A packer is to pack the quantized layer of the matrix to create a packed layer represented by a packed matrix, the packed matrix having non-zero values of the matrix grouped together along at least one of a row or a column of the matrix. A blocker is to block the packed layer into a blocked layer by dividing the non-zero values in the packed matrix into blocks. A fuser is to fuse the blocked layer into a pipeline. A packager is to package the pipeline into a binary.

公开(公告)号：US11507838B2

公开(公告)日：2022-11-22

申请号：US16456863

申请日：2019-06-28

Applicant: Intel Corporation

Inventor： Mikael Bourges-Sevenier ,  Adam Herr ,  Sridhar Sharma ,  Derek Gerstmann ,  Todd Anderson ,  Justin Gottschlich

IPC: G06N3/08 ,  G06F17/16

Abstract: Methods, apparatus, systems and articles of manufacture to optimize execution of a machine learning model are disclosed. An example apparatus includes a quantizer to quantize a layer of a model based on an execution constraint, the layer of the model represented by a matrix. A packer is to pack the quantized layer of the matrix to create a packed layer represented by a packed matrix, the packed matrix having non-zero values of the matrix grouped together along at least one of a row or a column of the matrix. A blocker is to block the packed layer into a blocked layer by dividing the non-zero values in the packed matrix into blocks. A fuser is to fuse the blocked layer into a pipeline. A packager is to package the pipeline into a binary.

公开(公告)号：US11493914B2

公开(公告)日：2022-11-08

申请号：US16913845

申请日：2020-06-26

Applicant: Intel Corporation

Inventor： Ignacio Alvarez ,  Todd Anderson ,  Vy Vo ,  Javier Felip Leon ,  Javier Perez-Ramirez

IPC: G05B23/02 ,  G05B13/02

Abstract: Systems, apparatuses and methods may provide for technology that obtains categorization information and corresponding uncertainty information from a perception subsystem, wherein the categorization information and the corresponding uncertainty information are to be associated with an object in an environment. The technology may also determine whether the corresponding uncertainty information satisfies one or more relevance criteria, and automatically control the perception subsystem to increase an accuracy in one or more subsequent categorizations of the object if the corresponding uncertainty information satisfies the one or more relevance criteria. In one example, determining whether the corresponding uncertainty information satisfies the relevance criteria includes taking a plurality of samples from the categorization information and the corresponding uncertainty information, generating a plurality of actuation plans based on the plurality of samples, and determining a safety deviation across the plurality of actuation plans, wherein the relevance criteria are satisfied if the safety deviation exceeds a threshold.

公开(公告)号：US20200327118A1

公开(公告)日：2020-10-15

申请号：US16914302

申请日：2020-06-27

Applicant: Intel Corporation

Inventor： Nesreen K. Ahmed ,  Dipanjan Sengupta ,  Todd Anderson ,  Theodore Willke

IPC: G06F16/248 ,  G06F16/901 ,  G06K9/62 ,  G06F8/41

Abstract: Systems, apparatuses and methods may provide for technology that identifies a query code, translates the query code into a query graph, generates a candidate vector based on a candidate graph, wherein the candidate graph is associated with a candidate code, generates a query vector based on the query graph, and determines a similarity measurement between the query vector and the candidate vector.