Intel Corporation

Jingyi Jin

Liwei Ma

Michael S Strickland

Chandrasekaran Sakthivel

Barath Lakshmanan

Jeremy Bottleson

Abhishek R Appu

Joydeep Ray

Altug Koker

Elmoustapha Ould-Ahmed-Vall

Justin E Gottschlich

Ben J Ashbaugh

Eriko Nurvitadhi

Nadathur Rajagopalan Satish

Farshad Akhbari

Prasoonkumar Surti

Balaji Vembu

Anbang Yao

Linda L Hurd

Tatiana Shpeisman

Xiaoming Chen

Ping Tak Tang

Mike B Macpherson

Javier Sebastian Turek

Kevin Nealis

Brian T Lewis

Dhawal Srivastava

Lindsey Kuper

Naila Farooqui

Barath Lakshamanan

Michael Strikland

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 library of machine learning primitives is provided to optimize a machine learning model to improve the efficiency of inference operations. In one embodiment a trained convolutional neural network (CNN) model is processed into a trained CNN model via pruning, convolution window optimization, and quantization.

Convolutional neural network optimization mechanism

An apparatus to facilitate optimization of a convolutional neural network (CNN) is disclosed. The apparatus includes optimization logic to receive a CNN model having a list of instructions and including pruning logic to optimize the list of instructions by eliminating branches in the list of instructions that comprise a weight value of 0.

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

Embodiments provide systems and methods which facilitate optimization of a convolutional neural network (CNN). One embodiment provides for a non-transitory machine-readable medium storing instructions that cause one or more processors to perform operations comprising processing a trained convolutional neural network (CNN) to generate a processed CNN, the trained CNN having weights in a floating-point format. Processing the trained CNN includes quantizing the weights in the floating-point format to generate weights in an integer format. Quantizing the weights includes generating a quantization table to enable non-uniform quantization of the weights and quantizing the weights from the floating-point format to the integer format using the quantization table. The operations additionally comprise performing an inference operation utilizing the processed CNN with the integer format weights.

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.

Growing community of inventors

Folsom, CA, United States of America

Jingyi Jin