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

Sara S Baghsorkhi

Radhakrishnan Venkataraman

John Charles Weast

Ankur N Shah

Linda L Hurd

Ben J Ashbaugh

Deepak Shashidhar Vembar

Liwei Ma

James M Holland

Michael Apodaca

Adam T Lake

Kai Xiao

Jeffery S Boles

Atsuo Kuwahara

Dukhwan Kim

Brent E Insko

Anupreet S Kalra

Hugues Labbe

Eriko Nurvitadhi

Mike B Macpherson

Feng Chen

Nadathur Rajagopalan Satish

Farshad Akhbari

Vasanth Ranganathan

Sanjeev S Jahagirdar

Narayan Srinivasa

Nikos Kaburlasos

Nicolas C Galoppo Von Borries

Jacek Kwiatkowski

Pattabhiraman K

Philip R Laws

Sayan Lahiri

Jingyi Jin

Rajkishore Barik

Michael S Strickland

Barath Lakshmanan

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

Jeremy Bottleson

Anil Thomas

Jayaram Bobba

Kumar Balasubramanian

Jerin C Justin

Gokce Keskin

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

Wenyin Fu

Bhushan M Borole

Javier Sebastian Turek

David M Cimini

Louis Feng

Brian T Lewis

Vivek Tiwari

Lindsey Kuper

Naila Farooqui

Barath Lakshamanan

Xiaoming Chen

Dipankar Das

Kevin Nealis

Srinivas Sridharan

Karthikeyan Vaidyanathan

Mikhail E Smorkalov

Anbang Yao

Yan E Wang

Jin Guo

Derek John Foster

Tri Thanh Khuong

Hughes Labbe

Kamalakar V Pawar

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

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

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.

Coordination and increased utilization of graphics processors during inference

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.

Communication optimizations for distributed machine learning

A mechanism is described for facilitating sharing of data and compression expansion of models at autonomous machines. A method of embodiments, as described herein, includes detecting a first processor processing information relating to a neural network at a first computing device, where the first processor comprises a first graphics processor and the first computing device comprises a first autonomous machine. The method further includes facilitating the first processor to store one or more portions of the information in a library at a database, where the one or more portions are accessible to a second processor of a computing device.

Efficient sharing and compression expansion of data across processing systems

In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.

Extend GPU/CPU coherency to multi-GPU cores

A mechanism is described for facilitating storage management for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting one or more components associated with machine learning, where the one or more components include memory and a processor coupled to the memory, and where the processor includes a graphics processor. The method may further include allocating a storage portion of the memory and a hardware portion of the processor to a machine learning training set, where the storage and hardware portions are precise for implementation and processing of the training set.

Storage management for machine learning at autonomous machines

Growing community of inventors

Sunnyvale, CA, United States of America

Chandrasekaran Sakthivel