Intel Corporation

Qognify Ltd.

Nice Systems Ltd.

Guy Boudoukh

Robert Valentine

Mark Jay Charney

Zeev Sperber

Rajesh Poornachandran

Doron Girmonsky

Raanan Yehezkel

Yaniv Gurwicz

Christopher J Hughes

Amit Gradstein

Alexander F Heinecke

Simon Rubanovich

Eriko Nurvitadhi

Menachem Adelman

Dan Baum

Naveen K Mellempudi

Nilesh K Jain

Brinda Ganesh

Rinat Rappoport

Vladimir Goldner

Guy Blumstein-Koren

Evangelos Georganas

Barukh Ziv

Tony Lee Werner

Omid Azizi

Chen Koren

Arik Narkis

Andrew Yang

Michael Rotzin

Regev Shemy

Uri Sherman

Yash Akhauri

Shahar Mizrahi

Dana Rip

Yaroslav Pollak

Systems, methods, and apparatuses relating sparsity based FMA. In some examples, an instance of a single FMA instruction has one or more fields for an opcode, one or more fields to identify a source/destination matrix operand, one or more fields to identify a first plurality of source matrix operands, one or more fields to identify a second plurality of matrix operands, wherein the opcode is to indicate that execution circuitry is to select a proper subset of FP8 data elements from the first plurality of source matrix operands based on sparsity controls from a first matrix operand of the second plurality of matrix operands and perform a FMA.

Instructions for structured-sparse tile matrix FMA

Disclosed embodiments relate to sparse matrix multiplication (SMM) acceleration using column folding and squeezing. In one example, a processor, in response to a SMM instruction having fields to specify locations of first, second, and output matrices, the second matrix being a sparse matrix, uses execution circuitry to pack the second matrix by replacing one or more zero-valued elements with non-zero elements yet to be processed, each of the replaced elements further including a field to identify its logical position within the second matrix, and, the execution circuitry further to, for each non-zero element at row M and column K of the specified first matrix, generate a product of the element and each corresponding non-zero element at row K, column N of the packed second matrix, and accumulate each generated product with a previous value of a corresponding element at row M and column N of the specified output matrix.

Matrix multiplication acceleration of sparse matrices using column folding and squeezing

A system and method for detecting an object of interest. A system and method may generate a first signature for an object of interest based on an image of the object of interest. A system and method may generate a second signature for a candidate object based on an image of the candidate object. A system and method may calculate a similarity score by relating the first signature to the second signature and may determine the image of the candidate object is an image of the object of interest based on the similarity score.

System and method for suspect search

The subject matter discloses a method, comprising obtaining a scene from a video camera and defining at least one point of the scene; creating a first scene terrain model of the scene, said first scene terrain model comprises a typical human object size in pixels in said at least one point of the scene; creating a second scene model of the scene, said second model defines a false positive determination that said at least one point comprises a human object; wherein said second scene model is created when the scene does not contain a human object; obtaining an image of the scene, said image is captured by a video camera; determining whether the human object is detected at the at least one point of said captured image by applying said first scene terrain model and said second scene model on the least one point.

Method and system for improving surveillance of PTZ cameras

Growing community of inventors

Ramat Hasharon, Israel

Guy Boudoukh