Intel Corporation

Dukhwan Kim

Altug Koker

Abhishek R Appu

Joydeep Ray

Linda L Hurd

Feng Chen

Farshad Akhbari

Narayan Srinivasa

Nadathur Rajagopalan Satish

Sara S Baghsorkhi

John Charles Weast

Mike B Macpherson

Xiaoming Chen

Balaji Vembu

Kamal Sinha

Anbang Yao

Justin E Gottschlich

Ping Tak Tang

Michael S Strickland

Prasoonkumar Surti

Eriko Nurvitadhi

Ben J Ashbaugh

Elmoustapha Ould-Ahmed-Vall

Nicolas C Galoppo Von Borries

Rajkishore Barik

Tsung-Han Lin

Liwei Ma

Tatiana Shpeisman

Kevin Nealis

Vasanth Ranganathan

Sanjeev S Jahagirdar

Barath Lakshmanan

Subramaniam M Maiyuran

Travis T Schluessler

Karthik Vaidyanathan

Adam T Lake

John H Feit

Jeffery S Boles

Devan Burke

Chandrasekaran Sakthivel

Wenyin Fu

Josh B Mastronarde

Jeremy Bottleson

Narayan Biswal

Stanley J Baran

Gokcen Cilingir

Nilesh V Shah

Barnan Das

Archie Sharma

Mayuresh M Varerkar

Sherine Abdelhak

Neelay Pandit

Praneetha Kotha

Sachin B Godse

Tae-Hoon Kim

Yi-Jen Chiu

Mike MacPherson

Ke Chen

Bongjin Jun

Anhang Yao

Nadathur Rajagoplan Satish

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

One embodiment provides a general-purpose graphics processing unit comprising a dynamic precision floating-point unit including a control unit having precision tracking hardware logic to track an available number of bits of precision for computed data relative to a target precision, wherein the dynamic precision floating-point unit includes computational logic to output data at multiple precisions.

Compute optimizations for low precision machine learning operations

In an example, an apparatus comprises a compute engine comprising a high precision component and a low precision component; and logic, at least partially including hardware logic, to receive instructions in the compute engine; select at least one of the high precision component or the low precision component to execute the instructions; and apply a gate to at least one of the high precision component or the low precision component to execute the instructions. Other embodiments are also disclosed and claimed.

Dynamic precision for neural network compute operations

Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Compute optimization mechanism for deep neural networks

One embodiment provides a multi-chip module accelerator usable to execute tensor data processing operations a multi-chip module. The multi-chip module may include a memory stack including multiple memory dies and parallel processor circuitry communicatively coupled to the memory stack. The parallel processor circuitry may include multiprocessor cores to execute matrix multiplication and accumulate operations. The matrix multiplication and accumulate operations may include floating-point operations that are configurable to include two-dimensional matrix multiply and accumulate operations involving inputs that have differing floating-point precisions. The floating-point operations may include a first operation at a first precision and a second operation at a second precision. The first operation may include a multiply having at least one 16-bit floating-point input and the second operation may include an accumulate having a 32-bit floating-point input.

One embodiment provides a parallel processor comprising a hardware scheduler to schedule pipeline commands for compute operations to one or more of multiple types of compute units, a plurality of processing resources including a first sparse compute unit configured for input at a first level of sparsity and hybrid memory circuitry including a memory controller, a memory interface, and a second sparse compute unit configured for input at a second level of sparsity that is greater than the first level of sparsity.

Programmable coarse grained and sparse matrix compute hardware with advanced scheduling

An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core including mixed-precision execution circuitry to execute one or more of the mixed-precision instructions to perform a mixed-precision dot-product operation comprising to perform a set of multiply and accumulate operations.

Compute optimization mechanism

A method of embodiments, as described herein, includes detecting thread groups relating to machine learning associated with one or more processing devices. The method may further include facilitating barrier synchronization of the thread groups across multiple dies such that each thread in a thread group is scheduled across a set of compute elements associated with the multiple dies, where each die represents a processing device of the one or more processing devices, the processing device including a graphics processor.

Barriers and synchronization for machine learning at autonomous machines

One embodiment provides an apparatus comprising a memory stack including multiple memory dies and a parallel processor including a plurality of multiprocessors. Each multiprocessor has a single instruction, multiple thread (SIMT) architecture, the parallel processor coupled to the memory stack via one or more memory interfaces. At least one multiprocessor comprises a multiply-accumulate circuit to perform multiply-accumulate operations on matrix data in a stage of a neural network implementation to produce a result matrix comprising a plurality of matrix data elements at a first precision, precision tracking logic to evaluate metrics associated with the matrix data elements and indicate if an optimization is to be performed for representing data at a second stage of the neural network implementation, and a numerical transform unit to dynamically perform a numerical transform operation on the matrix data elements based on the indication to produce transformed matrix data elements at a second precision.

A graphics processor can include a processing cluster array including a plurality of processing clusters coupled with the plurality of memory controllers, each processing cluster of the plurality of processing clusters including a plurality of streaming multiprocessors, the processing cluster array configured for partitioning into a plurality of partitions. The plurality of partitions include a first partition including a first plurality of streaming multiprocessors configured to perform operations for a first neural network, The operations for the first neural network are isolated to the first partition. The plurality of partitions also include a second partition including a second plurality of streaming multiprocessors configured to perform operations for a second neural network. The operations for the second neural network are isolated to the second partition and protected from operations performed for the first neural network.

Graphics neural network processor, method, and system

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

An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.

Graphics processing integrated circuit package

One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction, the decoded instruction to cause the compute apparatus to perform a complex compute operation.

Growing community of inventors

San Jose, CA, United States of America

Dukhwan Kim