Intel Corporation

Sun Microsystems, Inc.

Xerox Corporation

Brian T Lewis

Rajkishore Barik

Prasoonkumar Surti

Feng Chen

Xiaoming Chen

Murali Sundaresan

Abhishek R Appu

Altug Koker

Balaji Vembu

Justin E Gottschlich

Tatiana Shpeisman

Mike B Macpherson

Jeffrey R Jackson

Joydeep Ray

Elmoustapha Ould-Ahmed-Vall

Linda L Hurd

Ben J Ashbaugh

Chandrasekaran Sakthivel

Michael S Strickland

Liwei Ma

Javier Sebastian Turek

Jingyi Jin

Lindsey Kuper

Naila Farooqui

Barath Lakshamanan

Subramaniam M Maiyuran

Murali Ramadoss

Guei-Yuan Lueh

Adam T Lake

Kun Tian

Eric C Samson

David M Puffer

Mike MacPherson

Graham Hamilton

David J Cowperthwaite

Bernd J W Mathiske

Sara A Bly

Michael L Tallan

Michael D Kupfer

Leonard Truong

Rashid Kaleem

Stephen B Tom

Zhi Hu Wang

Bratin Saha

Zhi Jun Wang

Barath Lakshmanan

Neal M Gafter

Theodore Charles Goldstein

Dong-Yuan Chen

Michael J Jordan

David M Bristor

James M Stichnoth

Antonios Printczis

Malcolm P Atkinson

William J Maybury

Jeffery D Hodges

A Brady Farrand

Jeffrey D Hodges

A mechanism is described for facilitating efficient training of neural networks at computing devices. A method of embodiments, as described herein, includes detecting one or more inputs for training of a neural network, and introducing randomness in floating point (FP) numbers to prevent overtraining of the neural network, where introducing randomness includes replacing less-significant low-order bits of operand and result values with new low-order bits during the training of the neural network.

Training with adaptive runtime and precision profiling

A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine an optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.

Tool for facilitating efficiency in machine learning

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

A mechanism is described for facilitating smart collection of data and smart management of autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and combining a first computation directed to be performed locally at a local computing device with a second computation directed to be performed remotely at a remote computing device in communication with the local computing device over the one or more networks, where the first computation consumes low power, wherein the second computation consumes high power.

Smart autonomous machines utilizing cloud, error corrections, and predictions

A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.

An apparatus to facilitate data prefetching is disclosed. The apparatus includes a memory, one or more execution units (EUs) to execute a plurality of processing threads and prefetch logic to prefetch pages of data from the memory to assist in the execution of the plurality of processing threads.

Thread prefetch mechanism

One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Generally, this disclosure provides systems, devices, methods and computer readable media for adaptive scheduling of task assignment among heterogeneous processor cores. The system may include any number of CPUs, a graphics processing unit (GPU) and memory configured to store a pool of work items to be shared by the CPUs and GPU. The system may also include a GPU proxy profiling module associated with one of the CPUs to profile execution of a first portion of the work items on the GPU. The system may further include profiling modules, each associated with one of the CPUs, to profile execution of a second portion of the work items on each of the CPUs. The measured profiling information from the CPU profiling modules and the GPU proxy profiling module is used to calculate a distribution ratio for execution of a remaining portion of the work items between the CPUs and the GPU.

Adaptive scheduling for task assignment among heterogeneous processor cores

Generally, this disclosure provides systems, devices, methods and computer readable media for implementing function callback requests between a first processor (e.g., a GPU) and a second processor (e.g., a CPU). The system may include a shared virtual memory (SVM) coupled to the first and second processors, the SVM configured to store at least one double-ended queue (Deque). An execution unit (EU) of the first processor may be associated with a first of the Deques and configured to push the callback requests to that first Deque. A request handler thread executing on the second processor may be configured to: pop one of the callback requests from the first Deque; execute a function specified by the popped callback request; and generate a completion signal to the EU in response to completion of the function.

Function callback mechanism between a Central Processing Unit (CPU) and an auxiliary processor

Autonomous machines through cloud, error corrections, and predictions

An example system for adaptive scheduling of task assignment among heterogeneous processor cores may include any number of CPUs, a graphics processing unit (GPU) and memory configured to store a pool of work items to be shared by the CPUs and GPU. The system may also include a GPU proxy profiling module associated with one of the CPU s to profile execution of a first portion of the work items on the GPU. The system may further include profiling modules, each associated with one of the CPUs, to profile execution of a second portion of the work items on each of the CPUs. The measured profiling information from the CPU profiling modules and the GPU proxy profiling module is used to calculate a distribution ratio for execution of a remaining portion of the work items between the CPUs and the GPU.

Growing community of inventors

Palo Alto, CA, United States of America

Brian T Lewis