公开(公告)号：US12205217B2

公开(公告)日：2025-01-21

申请号：US17514485

申请日：2021-10-29

Applicant: Intel Corporation

Inventor： Honnesh Rohmetra ,  Carl S. Marshall ,  Selvakumar Panneer

IPC: G06T15/00 ,  G06N3/08 ,  G06T7/10 ,  H04N21/81

Abstract: One embodiment provides a method comprising, at a runtime library executed by a processor of a data processing system, receiving an input frame having objects to be stylized via a style transfer network associated with the runtime library, wherein the style transfer network is a neural network model trained to apply one or more visual styles to an input frame, performing instance segmentation on the input frame to generate one or more instance masks to identify one or more objects to be stylized, generating one or more stylized frames for each style to transfer to the input frame, and merging, via the one or more instance masks, stylized objects from one or more stylized frames with un-stylized content from the input frame to generate an output frame with per-instance stylization.

公开(公告)号：US11995029B2

公开(公告)日：2024-05-28

申请号：US17428527

申请日：2020-03-14

Applicant: Intel Corporation

Inventor： Lakshminarayanan Striramassarma ,  Prasoonkumar Surti ,  Varghese George ,  Ben Ashbaugh ,  Aravindh Anantaraman ,  Valentin Andrei ,  Abhishek Appu ,  Nicolas Galoppo Von Borries ,  Altug Koker ,  Mike Macpherson ,  Subramaniam Maiyuran ,  Nilay Mistry ,  Elmoustapha Ould-Ahmed-Vall ,  Selvakumar Panneer ,  Vasanth Ranganathan ,  Joydeep Ray ,  Ankur Shah ,  Saurabh Tangri

IPC: G06F12/00 ,  G06F7/544 ,  G06F7/575 ,  G06F7/58 ,  G06F9/30 ,  G06F9/38 ,  G06F9/50 ,  G06F12/02 ,  G06F12/06 ,  G06F12/0802 ,  G06F12/0804 ,  G06F12/0811 ,  G06F12/0862 ,  G06F12/0866 ,  G06F12/0871 ,  G06F12/0875 ,  G06F12/0882 ,  G06F12/0888 ,  G06F12/0891 ,  G06F12/0893 ,  G06F12/0895 ,  G06F12/0897 ,  G06F12/1009 ,  G06F12/128 ,  G06F15/78 ,  G06F15/80 ,  G06F17/16 ,  G06F17/18 ,  G06T1/20 ,  G06T1/60 ,  H03M7/46 ,  G06N3/08 ,  G06T15/06

CPC classification number: G06F15/7839 ,  G06F7/5443 ,  G06F7/575 ,  G06F7/588 ,  G06F9/3001 ,  G06F9/30014 ,  G06F9/30036 ,  G06F9/3004 ,  G06F9/30043 ,  G06F9/30047 ,  G06F9/30065 ,  G06F9/30079 ,  G06F9/3887 ,  G06F9/5011 ,  G06F9/5077 ,  G06F12/0215 ,  G06F12/0238 ,  G06F12/0246 ,  G06F12/0607 ,  G06F12/0802 ,  G06F12/0804 ,  G06F12/0811 ,  G06F12/0862 ,  G06F12/0866 ,  G06F12/0871 ,  G06F12/0875 ,  G06F12/0882 ,  G06F12/0888 ,  G06F12/0891 ,  G06F12/0893 ,  G06F12/0895 ,  G06F12/0897 ,  G06F12/1009 ,  G06F12/128 ,  G06F15/8046 ,  G06F17/16 ,  G06F17/18 ,  G06T1/20 ,  G06T1/60 ,  H03M7/46 ,  G06F9/3802 ,  G06F9/3818 ,  G06F9/3867 ,  G06F2212/1008 ,  G06F2212/1021 ,  G06F2212/1044 ,  G06F2212/302 ,  G06F2212/401 ,  G06F2212/455 ,  G06F2212/60 ,  G06N3/08 ,  G06T15/06

Abstract: Multi-tile Memory Management for Detecting Cross Tile Access, Providing Multi-Tile Inference Scaling with multicasting of data via copy operation, and Providing Page Migration are disclosed herein. In one embodiment, a graphics processor for a multi-tile architecture includes a first graphics processing unit (GPU) having a memory and a memory controller, a second graphics processing unit (GPU) having a memory and a cross-GPU fabric to communicatively couple the first and second GPUs. The memory controller is configured to determine whether frequent cross tile memory accesses occur from the first GPU to the memory of the second GPU in the multi-GPU configuration and to send a message to initiate a data transfer mechanism when frequent cross tile memory accesses occur from the first GPU to the memory of the second GPU.

公开(公告)号：US11989595B2

公开(公告)日：2024-05-21

申请号：US17526097

申请日：2021-11-15

Applicant: Intel Corporation

Inventor： Reshma Lal ,  Pradeep Pappachan ,  Luis Kida ,  Soham Jayesh Desai ,  Sujoy Sen ,  Selvakumar Panneer ,  Robert Sharp

IPC: G06F9/50 ,  G06F9/38 ,  G06T1/20 ,  G06T1/60

CPC classification number: G06F9/5083 ,  G06F9/3814 ,  G06F9/5027 ,  G06T1/20 ,  G06T1/60

Abstract: An apparatus to facilitate disaggregated computing for a distributed confidential computing environment is disclosed. The apparatus includes one or more processors to: provide a remote GPU middleware layer to act as a proxy for an application stack on a client platform separate from the apparatus; communicate, by the remote GPU middleware layer, with a kernel mode driver of the one or more processors to cause the host memory to be allocated for command buffers and data structures received from the client platform for consumption by a command streamer of a remote GPU of the apparatus; and invoke, by the remote GPU middleware layer, the kernel mode driver to submit a workload generated by the application stack, the workload submitted for processing by the remote GPU using the command buffers and the data structures allocated in the host memory as directed by the command streamer.

公开(公告)号：US20220405888A1

公开(公告)日：2022-12-22

申请号：US17354186

申请日：2021-06-22

Applicant: Intel Corporation

Inventor： Satyam Srivastava ,  Saurabh Tangri ,  Rajeev Nalawadi ,  Carl S. Marshall ,  Selvakumar Panneer

IPC: G06T5/00 ,  G06T5/50 ,  G06T3/40 ,  G06N3/08 ,  G06N3/04

Abstract: An apparatus to facilitate video motion smoothing is disclosed. The apparatus comprises one or more processors including a graphics processor, the one or more processors including circuitry configured to receive a video stream, decode the video stream to generate a motion vector map and a plurality of video image frames, analyze the motion vector map to detect a plurality of candidate frames, wherein the plurality of candidate frames comprise a period of discontinuous motion in the plurality of video image frames and the plurality of candidate frames are determined based on a classification generated via a convolutional neural network (CNN), generate, via a generative adversarial network (GAN), one or more synthetic frames based on the plurality of candidate frames, insert the one or more synthetic frames between the plurality of candidate frames to generate up-sampled video frames and transmit the up-sampled video frames for display.

公开(公告)号：US11526964B2

公开(公告)日：2022-12-13

申请号：US16898116

申请日：2020-06-10

Applicant: Intel Corporation

Inventor： Daniel Pohl ,  Carl Marshall ,  Selvakumar Panneer

IPC: G06T5/00 ,  G06N3/08 ,  G06N3/04 ,  G06T15/00 ,  G06T1/20

Abstract: An apparatus to facilitate deep learning based selection of samples for adaptive supersampling is disclosed. The apparatus includes one or more processing elements to: receive training data comprising input tiles and corresponding supersampling values for the input tiles, wherein each input tile comprises a plurality of pixels, and train, based on the training data, a machine learning model to identify a level of supersampling for a rendered tile of pixels.

公开(公告)号：US20220100580A1

公开(公告)日：2022-03-31

申请号：US17526097

申请日：2021-11-15

Applicant: Intel Corporation

Inventor： Reshma Lal ,  Pradeep Pappachan ,  Luis Kida ,  Soham Jayesh Desai ,  Sujoy Sen ,  Selvakumar Panneer ,  Robert Sharp

IPC: G06F9/50 ,  G06T1/60 ,  G06T1/20 ,  G06F9/38

Abstract: An apparatus to facilitate disaggregated computing for a distributed confidential computing environment is disclosed. The apparatus includes one or more processors to: provide a remote GPU middleware layer to act as a proxy for an application stack on a client platform separate from the apparatus; communicate, by the remote GPU middleware layer, with a kernel mode driver of the one or more processors to cause the host memory to be allocated for command buffers and data structures received from the client platform for consumption by a command streamer of a remote GPU of the apparatus; and invoke, by the remote GPU middleware layer, the kernel mode driver to submit a workload generated by the application stack, the workload submitted for processing by the remote GPU using the command buffers and the data structures allocated in the host memory as directed by the command streamer.

公开(公告)号：US20210403004A1

公开(公告)日：2021-12-30

申请号：US17471411

申请日：2021-09-10

Applicant: Intel Corporation

Inventor： Ignacio J. Alvarez ,  Marcos Carranza ,  Ralf Graefe ,  Francesc Guim bernat ,  Cesar Martinez-spessot ,  Dario Oliver ,  Selvakumar Panneer ,  Michael Paulitsch ,  Rafael Rosales

IPC: B60W40/09 ,  G06K9/62 ,  G06N20/00 ,  H04W4/40

Abstract: Techniques are disclosed to address issues related to the use of personalized training data to supplement machine learning trained models for Driver Monitoring System (DMS), and the accompanying mechanisms to maintain confidentiality of this personalized training data. The techniques disclosed herein also address issues related to maintaining transparency with respect to collected sensor data used in a DMS. Additionally, the techniques disclosed herein facilitate the generation of a digital representation of a driver for use as supplemental training data for the DMS machine learning trained models, which allow for DMS algorithms to be tailored to individual users.

公开(公告)号：US20210117247A1

公开(公告)日：2021-04-22

申请号：US17133716

申请日：2020-12-24

Applicant: Intel Corporation

Inventor： Selvakumar Panneer ,  Pradeep Pappachan ,  Reshma Lal

IPC: G06F9/50 ,  G06F21/60 ,  H04L9/08 ,  G06T1/20

Abstract: A computing platform is disclosed. The computing platform includes a first computer system comprising a first graphics processing unit (GPU), a network coupled to the first computer system and a second computer system, coupled to the first computer system via the network, comprising a second GPU, wherein the first and second computer system are configured to perform distributed processing of graphics workloads between the first GPU and the second GPU.

公开(公告)号：US10861225B2

公开(公告)日：2020-12-08

申请号：US16234463

申请日：2018-12-27

Applicant: Intel Corporation

Inventor： Jill Boyce ,  Soethiha Soe ,  Selvakumar Panneer ,  Adam Lake ,  Nilesh Jain ,  Deepak Vembar ,  Glen J. Anderson ,  Varghese George ,  Carl Marshall ,  Scott Janus ,  Saurabh Tangri ,  Karthik Veeramani ,  Prasoonkumar Surti

IPC: G06T17/00 ,  G06N3/04 ,  G06T7/20 ,  G06N3/08 ,  G06K9/72 ,  G06F3/01 ,  G06K9/62

Abstract: Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.

公开(公告)号：US20190102047A1

公开(公告)日：2019-04-04

申请号：US15721768

申请日：2017-09-30

Applicant: Intel Corporation

Inventor： Glen J. Anderson ,  Giuseppe Raffa ,  Sangita Sharma ,  Carl S. Marshall ,  Meng Shi ,  Selvakumar Panneer

IPC: G06F3/0481 ,  G06F17/30

Abstract: Systems, apparatuses and methods for technology that provides smart work spaces in ubiquitous computing environments. The technology may determine a task to be performed in a smart work space and perform task modeling, wherein the task modeling includes determining one or more user interfaces involved with the task. One or more placements may be determined for the one or more user interfaces based on one or more ergonomic conditions, an incidence of an interaction, and a length of time of interaction. The technology may position the one or more user interfaces into the smart work space in accordance with the determined one or more placements.