Intel Corporation

Kamal Sinha

Altug Koker

Abhishek R Appu

Joydeep Ray

Balaji Vembu

Prasoonkumar Surti

Nicolas C Galoppo Von Borries

Eriko Nurvitadhi

Tsung-Han Lin

Vasanth Ranganathan

Rajkishore Barik

Wenyin Fu

Sanjeev S Jahagirdar

Elmoustapha Ould-Ahmed-Vall

Bhushan M Borole

Subramaniam M Maiyuran

Travis T Schluessler

Narayan Srinivasa

Feng Chen

Sara S Baghsorkhi

Linda L Hurd

Dukhwan Kim

Justin E Gottschlich

Farshad Akhbari

Nadathur Rajagopalan Satish

Ben J Ashbaugh

Kiran Chand Veernapu

Anbang Yao

Xiaoming Chen

Devan Burke

Nikos Kaburlasos

John H Feit

Chandrasekaran Sakthivel

John Charles Weast

Adam T Lake

Jeffery S Boles

Josh B Mastronarde

Tatiana Shpeisman

Ping Tak Tang

Michael S Strickland

Pattabhiraman K

Eric J Hoekstra

Ankur N Shah

Aravindh V Anantaraman

Karthik Vaidyanathan

Scott R Janus

Lakshminarayanan Striramassarma

Brent E Insko

Kevin Nealis

Arthur Hunter, Jr

Eric J Asperheim

Sanu K Mathew

Mark A Anders

Himanshu Kaul

David M Puffer

Peter L Doyle

Mike MacPherson

Philip R Laws

Jeremy Bottleson

Kai Xiao

James M Holland

Liwei Ma

Mike B Macpherson

Valentin Andrei

Iqbal Rajwani

Radhakrishnan Venkataraman

Anupama Ambardar Thaploo

Sayan Lahiri

Guei-Yuan Lueh

Abhishek Venkatesh

Jacek Kwiatkowski

Varghese George

Murali Ramadoss

James A Valerio

Jonathan Kennedy

Vikranth Vemulapalli

Jonathan D Pearce

Deepak Shashidhar Vembar

Vivek Tiwari

Michael Apodaca

Daniel Pohl

Tomasz Janczak

Supratim Pal

Sungye Kim

Saurabh Sharma

William B Sadler

Slawomir Grajewski

Yoav Harel

Sunil Nekkanti

Marian Alin Petre

Shailesh Shah

Rajikshore Barik

Bee Ngo

Selvakumar Panneer

Niranjan L Cooray

Jaydeep P Kulkarni

Carson Brownlee

Jefferson Amstutz

Saurabh Tangri

Navid Toosizadeh

One embodiment provides for a compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction that specifies multiple operands including a multi-bit input value and a one-bit weight associated with a neural network, as well as an arithmetic logic unit including a multiplier, an adder, and an accumulator register. To execute the decoded instruction, the multiplier is to perform a fused operation including an exclusive not OR (XNOR) operation and a population count operation. The adder is configured to add the intermediate product to a value stored in the accumulator register and update the value stored in the accumulator register.

Compute optimizations for neural networks

In one embodiment, a processor includes: a graphics processor to execute a workload; and a power controller coupled to the graphics processor. The power controller may include a voltage ramp circuit to receive a request for the graphics processor to operate at a first performance state having a first operating voltage and a first operating frequency and cause an output voltage of a voltage regulator to increase to the first operating voltage. The voltage ramp circuit may be configured to enable the graphics processor to execute the workload at an interim performance state having an interim operating voltage and an interim operating frequency when the output voltage reaches a minimum operating voltage. Other embodiments are described and claimed.

System, apparatus and method for increasing performance in a processor during a voltage ramp

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

One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.

Instructions and logic to perform floating point and integer operations for machine learning

A mechanism is described for facilitating intelligent thread scheduling at autonomous machines. A method of embodiments, as described herein, includes detecting dependency information relating to a plurality of threads corresponding to a plurality of workloads associated with tasks relating to a processor including a graphics processor. The method may further include generating a tree of thread groups based on the dependency information, where each thread group includes multiple threads, and scheduling one or more of the thread groups associated a similar dependency to avoid dependency conflicts.

Efficient thread group scheduling

Systems and methods for improving cache efficiency and utilization are disclosed. In one embodiment, a graphics processor includes processing resources to perform graphics operations and a cache controller of a cache coupled to the processing resources. The cache controller is configured to control cache priority by determining whether default settings or an instruction will control cache operations for the cache.

Systems and methods for improving cache efficiency and utilization

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

Methods and apparatus relating to techniques for multi-tile memory management. In an example, a graphics processor includes an interposer, a first chiplet coupled with the interposer, the first chiplet including a graphics processing resource and an interconnect network coupled with the graphics processing resource, cache circuitry coupled with the graphics processing resource via the interconnect network, and a second chiplet coupled with the first chiplet via the interposer, the second chiplet including a memory-side cache and a memory controller coupled with the memory-side cache. The memory controller is configured to enable access to a high-bandwidth memory (HBM) device, the memory-side cache is configured to cache data associated with a memory access performed via the memory controller, and the cache circuitry is logically positioned between the graphics processing resource and a chiplet interface.

Multi-tile memory management

One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute a 32-bit intermediate product of 16-bit operands and to compute a 32-bit sum based on the 32-bit intermediate product.

Techniques to improve performance of matrix multiply operations are described in which a compute kernel can specify one or more element-wise operations to perform on output of the compute kernel before the output is transferred to higher levels of a processor memory hierarchy.

Machine learning sparse computation mechanism

Methods and apparatus relating to techniques for avoiding cache lookup for cold cache. In an example, an apparatus comprises logic, at least partially comprising hardware logic, to collect user information for a user of a data processing device, generate a user profile for the user of the data processing device from the user information, and set a power profile a processor in the data processing device using the user profile. Other embodiments are also disclosed and claimed.

Processor power management

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

Methods and apparatus relating to techniques for multi-tile memory management. In an example, an apparatus comprises a cache memory, a high-bandwidth memory, a shader core communicatively coupled to the cache memory and comprising a processing element to decompress a first data element extracted from an in-memory database in the cache memory and having a first bit length to generate a second data element having a second bit length, greater than the first bit length, and an arithmetic logic unit (ALU) to compare the data element to a target value provided in a query of the in-memory database. Other embodiments are also disclosed and claimed.

Embodiments are generally directed to multi-tile graphics processor rendering. An embodiment of an apparatus includes a memory for storage of data; and one or more processors including a graphics processing unit (GPU) to process data, wherein the GPU includes a plurality of GPU tiles, wherein, upon geometric data being assigned to each of a plurality of screen tiles, the apparatus is to transfer the geometric data to the plurality of GPU tiles.

Multi-tile graphics processor rendering

In accordance with some embodiments, the render rate is varied across and/or up and down the display screen. This may be done based on where the user is looking in order to reduce power consumption and/or increase performance. Specifically the screen display is separated into regions, such as quadrants. Each of these regions is rendered at a rate determined by at least one of what the user is currently looking at, what the user has looked at in the past and/or what it is predicted that the user will look at next. Areas of less focus may be rendered at a lower rate, reducing power consumption in some embodiments.

Regional adjustment of render rate

Growing community of inventors

Rancho Cordova, CA, United States of America

Kamal Sinha