Intel Corporation

Broadcom Corporation

Chandrasekaran Sakthivel

Joydeep Ray

Abhishek R Appu

Prasoonkumar Surti

Elmoustapha Ould-Ahmed-Vall

Altug Koker

Justin E Gottschlich

Balaji Vembu

Kamal Sinha

John Charles Weast

Sara S Baghsorkhi

Radhakrishnan Venkataraman

Liwei Ma

Ankur N Shah

Linda L Hurd

Ben J Ashbaugh

Deepak Shashidhar Vembar

James M Holland

Michael Apodaca

Kai Xiao

Adam T Lake

Dukhwan Kim

Jeffery S Boles

Atsuo Kuwahara

Brent E Insko

Anupreet S Kalra

Eriko Nurvitadhi

Hugues Labbe

Mike B Macpherson

Nadathur Rajagopalan Satish

Farshad Akhbari

Vasanth Ranganathan

Sanjeev S Jahagirdar

Narayan Srinivasa

Feng Chen

Jingyi Jin

Nikos Kaburlasos

Nicolas C Galoppo Von Borries

Jacek Kwiatkowski

Michael S Strickland

Pattabhiraman K

Philip R Laws

Barath Lakshmanan

Sayan Lahiri

Rajkishore Barik

Jeremy Bottleson

Rajesh Poornachandran

Travis T Schluessler

Murali Ramadoss

Narayan Biswal

Stanley J Baran

Tsung-Han Lin

Jonathan Kennedy

Gokcen Cilingir

Nilesh V Shah

Barnan Das

Archie Sharma

Mayuresh M Varerkar

Oguz Hasan Elibol

Anil Thomas

Jayaram Bobba

Kumar Balasubramanian

Gokce Keskin

Jerin C Justin

Pradeep Ramani

Rama Harihara

Jing Huang

Xiaoming Cui

Timothy B Costa

Ting Gong

Guozhong Zhuang

Bhavani Subramanian

Subramaniam M Maiyuran

Peter L Doyle

John H Feit

Daniel Pohl

Devan Burke

Dipankar Das

Wenyin Fu

Bhushan M Borole

Javier Sebastian Turek

David M Cimini

Louis Feng

Brian T Lewis

Srinivas Sridharan

Vivek Tiwari

Karthikeyan Vaidyanathan

Lindsey Kuper

Naila Farooqui

Mikhail E Smorkalov

Barath Lakshamanan

Xiaoming Chen

Kevin Nealis

Anbang Yao

Yan E Wang

Jin Guo

Derek John Foster

Tri Thanh Khuong

Hughes Labbe

Kamalakar V Pawar

An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

Neural network scheduling mechanism

A mechanism is described for detecting, at training time, information related to one or more tasks to be performed by the one or more processors according to a training dataset for a neural network, analyzing the information to determine one or more portions of hardware of a processor of the one or more processors that is configurable to support the one or more tasks, configuring the hardware to pre-select the one or more portions to perform the one or more tasks, while other portions of the hardware remain available for other tasks, and monitoring utilization of the hardware via a hardware unit of the graphics processor and, via a scheduler of the graphics processor, adjusting allocation of the one or more tasks to the one or more portions of the hardware based on the utilization.

Coordination and increased utilization of graphics processors during inference

Embodiments described herein provide an apparatus comprising an interconnect switch configured to couple with a plurality of graphics processors via a plurality of point-to-point interconnects and one or more processors including a graphics processor coupled with the interconnect switch via a point-to-point interconnect of the plurality of point-to-point interconnects.

Communication optimizations for distributed machine learning

An apparatus to facilitate optimization of a neural network (NN) is disclosed. The apparatus includes optimization logic to define a NN topology having one or more macro layers, adjust the one or more macro layers to adapt to input and output components of the NN and train the NN based on the one or more macro layers.

Neural network optimization mechanism

An embodiment of an electronic processing system may include an application processor, persistent storage media communicatively coupled to the application processor, and a graphics subsystem communicatively coupled to the application processor. The graphics subsystem may include a first graphics engine to process a graphics workload, and a second graphics engine to offload at least a portion of the graphics workload from the first graphics engine. The second graphics engine may include a low precision compute engine. The system may further include a wearable display housing the second graphics engine. Other embodiments are disclosed and claimed.

Graphics system with additional context

Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Compression of machine learning models utilizing pseudo-labeled data training

Systems, apparatuses and methods may provide away to enhance an augmented reality (AR) and/or virtual reality (VR) user experience with environmental information captured from sensors located in one or more physical environments. More particularly, systems, apparatuses and methods may provide a way to track, by an eye tracker sensor, a gaze of a user, and capture, by the sensors, environmental information. The systems, apparatuses and methods may render feedback, by one or more feedback devices or display device, for a portion of the environment information based on the gaze of the user.

Sensory enhanced augmented reality and virtual reality device

An autonomous vehicle is provided that includes one or more processors configured to provide a local compute manager to manage execution of compute workloads associated with the autonomous vehicle. The local compute manager can perform various compute operations, including receiving offload of compute operations from to other compute nodes and offloading compute operations to other compute notes, where the other compute nodes can be other autonomous vehicles. The local compute manager can also facilitate autonomous navigation functionality.

Autonomous vehicle advanced sensing and response

Dynamic assignment of down sampling intervals for data stream processing

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 analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

Dynamic distributed training of machine learning models

An embodiment of a graphics apparatus may include a focus identifier to identify a focus area, and a color compressor to selectively compress color data based on the identified focus area. Another embodiment of a graphics apparatus may include a motion detector to detect motion of a real object, a motion predictor to predict a motion of the real object, and an object placer to place a virtual object relative to the real object based on the predicted motion of the real object. Another embodiment of a graphics apparatus may include a frame divider to divide a frame into viewports, a viewport prioritizer to prioritize the viewports, a renderer to render a viewport of the frame in order in accordance with the viewport priorities, and a viewport transmitter to transmit a completed rendered viewport. Other embodiments are disclosed and claimed.

Predictive viewport renderer and foveated color compressor

A mechanism is described for facilitating inference coordination and processing utilization for machine learning. A method of embodiments, as described herein, includes limiting execution of workloads for the respective contexts of a plurality of contexts to a specified subset of a plurality of processing resources of a processing system according to physical resource slices of the processing system that are associated with the respective contexts of the plurality of contexts.

Embodiments described herein provide a system to configure distributed training of a neural network, the system comprising memory to store a library to facilitate data transmission during distributed training of the neural network; a network interface to enable transmission and receipt of configuration data associated with a set of worker nodes, the worker nodes configured to perform distributed training of the neural network; and a processor to execute instructions provided by the library. The instructions cause the processor to create one or more groups of the worker nodes, the one or more groups of worker nodes to be created based on a communication pattern for messages to be transmitted between the worker nodes during distributed training of the neural network. The processor can transparently adjust communication paths between worker nodes based on the communication pattern.

Growing community of inventors

Sunnyvale, CA, United States of America

Chandrasekaran Sakthivel