Patents by Inventor Jorge Albericio Latorre
Jorge Albericio Latorre has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Publication number: 20230196662Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more circuits are to use one or more neural networks to adjust one or more pixel blending weights.Type: ApplicationFiled: December 20, 2021Publication date: June 22, 2023Inventors: Pietari Kaskela, Andrew Tao, Michael Ranzinger, David Tarjan, Jonathan Filip Gustav Granskog, Jorge Albericio Latorre
-
Patent number: 11489541Abstract: In artificial neural networks, and other similar applications, there is typically a large amount of data involved that is considered sparse data. Due to the large size of the data involved in such applications, it is helpful to compress the data to save bandwidth resources when transmitting the data and save memory resources when storing the data. Introduced herein is a compression technique that selects elements with significant values from data and restructures them into a structured sparse format. By generating metadata that enforces the structured sparse format and organizing the data according to the metadata, the introduced technique not only reduces the size of the data but also consistently places the data in a particular format. As such, hardware can be simplified and optimized to process the data much faster and much more efficiently than the conventional compression techniques that rely on a non-structured sparsity format.Type: GrantFiled: May 30, 2019Date of Patent: November 1, 2022Assignee: NVIDIA CorporationInventors: Jorge Albericio Latorre, Ming Y. Siu
-
Patent number: 11392829Abstract: Approaches in accordance with various embodiments provide for the processing of sparse matrices for mathematical and programmatic operations. In particular, various embodiments enforce sparsity constraints for performing sparse matrix multiply-add instruction (MMA) operations. Deep neural networks can exhibit significant sparsity in the data used in operations, both in the activations and weights. The computational load can be reduced by excluding zero-valued data elements. A sparsity constraint is applied across all submatrices of a sparse matrix, providing fine-grained structured sparsity that is evenly distributed across the matrix. The matrix may then be compressed since a minimum number of elements of the matrix are known to have zero value. Matrix operations are then performed using these matrices.Type: GrantFiled: April 2, 2019Date of Patent: July 19, 2022Assignee: NVIDIA CorporationInventors: Jeff Pool, Ganesh Venkatesh, Jorge Albericio Latorre, Jack Choquette, Ronny Krashinsky, John Tran, Feng Xie, Ming Y. Siu, Manan Patel
-
Patent number: 11379420Abstract: Compressed data is oftentimes beneficial for reducing the computing resources required, for example, to transmit and store data. The compression of data is particularly useful when dealing with sparse data (data that includes numerous zeros or near-zero values) and only non-zero values above a certain threshold have significance. When dealing with compressed data, oftentimes the data needs to be decompressed for processing (e.g., by deep learning networks or other applications configured to operate on sparse, or other uncompressed data). Instructions are disclosed for supporting the decompression of compressed data by a processing unit such as a CPU and GPU.Type: GrantFiled: March 20, 2019Date of Patent: July 5, 2022Assignee: NVIDIA CORPORATIONInventors: Jorge Albericio Latorre, Jack H. Choquette, Manan Maheshkumar Patel, Jeffrey Pool, Ming Y. Siu, Ronny Meir Krashinsky, Ganesh Venkatesh
-
Patent number: 11249727Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks (ANNs) perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation, which is commonly performed, is the transpose operation. Additionally, many such systems need to process many matrices and/or matrices that are large in size. For sparse matrices that hold few significant values and many values that can be ignored, transmitting and processing all the values in such matrices is wasteful. Thus, techniques are introduced for storing a sparse matrix in a compressed format that allows for a matrix transpose operation to be performed on the compressed matrix without having to first decompress the compressed matrix. By utilizing the introduced techniques, more matrix operations can be performed than conventional systems.Type: GrantFiled: October 19, 2020Date of Patent: February 15, 2022Assignee: Nvidia CorporationInventors: Jorge Albericio Latorre, Jeff Pool, David Garcia
-
Patent number: 11127167Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation is the transpose operation. Techniques are introduced for storing a matrix in a compressed format that allows, for example, a transpose operation to be performed during decompression. Thus, by utilizing the introduced techniques, transformations of compressed matrices such transposition can be achieved in a more effective way. Parallel processing may also be used to more efficiently compress and/or decompress.Type: GrantFiled: April 29, 2019Date of Patent: September 21, 2021Assignee: NVIDIA CorporationInventors: Michael Frumkin, Jeffrey Pool, Jorge Albericio Latorre
-
Publication number: 20210034332Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks (ANNs) perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation, which is commonly performed, is the transpose operation. Additionally, many such systems need to process many matrices and/or matrices that are large in size. For sparse matrices that hold few significant values and many values that can be ignored, transmitting and processing all the values in such matrices is wasteful. Thus, techniques are introduced for storing a sparse matrix in a compressed format that allows for a matrix transpose operation to be performed on the compressed matrix without having to first decompress the compressed matrix. By utilizing the introduced techniques, more matrix operations can be performed than conventional systems.Type: ApplicationFiled: October 19, 2020Publication date: February 4, 2021Inventors: Jorge Albericio Latorre, Jeff Pool, David Garcia
-
Patent number: 10860293Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks (ANNs) perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation, which is commonly performed, is the transpose operation. Additionally, many such systems need to process many matrices and/or matrices that are large in size. For sparse matrices that hold few significant values and many values that can be ignored, transmitting and processing all the values in such matrices is wasteful. Thus, techniques are introduced for storing a sparse matrix in a compressed format that allows for a matrix transpose operation to be performed on the compressed matrix without having to first decompress the compressed matrix. By utilizing the introduced techniques, more matrix operations can be performed than conventional systems.Type: GrantFiled: February 27, 2019Date of Patent: December 8, 2020Assignee: Nvidia CorporationInventors: Jorge Albericio Latorre, Jeff Pool, David Garcia
-
Publication number: 20200373941Abstract: In artificial neural networks, and other similar applications, there is typically a large amount of data involved that is considered sparse data. Due to the large size of the data involved in such applications, it is helpful to compress the data to save bandwidth resources when transmitting the data and save memory resources when storing the data. Introduced herein is a compression technique that selects elements with significant values from data and restructures them into a structured sparse format. By generating metadata that enforces the structured sparse format and organizing the data according to the metadata, the introduced technique not only reduces the size of the data but also consistently places the data in a particular format. As such, hardware can be simplified and optimized to process the data much faster and much more efficiently than the conventional compression techniques that rely on a non-structured sparsity format.Type: ApplicationFiled: May 30, 2019Publication date: November 26, 2020Inventors: Jorge Albericio Latorre, Ming Y. Siu
-
Publication number: 20200342632Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation is the transpose operation. Techniques are introduced for storing a matrix in a compressed format that allows, for example, a transpose operation to be performed during decompression. Thus, by utilizing the introduced techniques, transformations of compressed matrices such transposition can be achieved in a more effective way. Parallel processing may also be used to more efficiently compress and/or decompress.Type: ApplicationFiled: April 29, 2019Publication date: October 29, 2020Inventors: Michael FRUMKIN, Jeffrey POOL, Jorge ALBERICIO LATORRE
-
Publication number: 20200285618Abstract: Compressed data is oftentimes beneficial for reducing the computing resources required, for example, to transmit and store data. The compression of data is particularly useful when dealing with sparse data (data that includes numerous zeros or near-zero values) and only non-zero values above a certain threshold have significance. When dealing with compressed data, oftentimes the data needs to be decompressed for processing (e.g., by deep learning networks or other applications configured to operate on sparse, or other uncompressed data). Instructions are disclosed for supporting the decompression of compressed data by a processing unit such as a CPU and GPU.Type: ApplicationFiled: March 20, 2019Publication date: September 10, 2020Inventors: Jorge Albericio Latorre, Jack H. Choquette, Manan Maheshkumar Patel, Jeffrey Pool, Ming Y. Siu, Ronny Meir Krashinsky, Ganesh Venkatesh
-
Publication number: 20200272425Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks (ANNs) perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation, which is commonly performed, is the transpose operation. Additionally, many such systems need to process many matrices and/or matrices that are large in size. For sparse matrices that hold few significant values and many values that can be ignored, transmitting and processing all the values in such matrices is wasteful. Thus, techniques are introduced for storing a sparse matrix in a compressed format that allows for a matrix transpose operation to be performed on the compressed matrix without having to first decompress the compressed matrix. By utilizing the introduced techniques, more matrix operations can be performed than conventional systems.Type: ApplicationFiled: February 27, 2019Publication date: August 27, 2020Inventors: Jorge Albericio Latorre, Jeff Pool, David Garcia