Intel Corporation

Ravishankar Iyer

Andrew J Herdrich

Donald K Newell

Rameshkumar G Illikkal

Srihari Makineni

Li Zhao

Omesh Tickoo

Glen J Anderson

Mark R Francis

Carl S Marshall

Selvakumar Panneer

Edwin Verplanke

David I Poisner

Jeffrey D Chamberlain

Nilesh K Jain

Rajesh Poornachandran

David J Harriman

Julius Yuli Mandelblat

Michael J Espig

Francesc Guim Bernat

Kshitij Arun Doshi

Bret L Toll

Kapil Sood

Ronak Singhal

Gregory A Peek

Eliezer Weissmann

Tsung-Yuan Charles Tai

Michael E Kounavis

Mahesh U Wagh

Tobias Max Kohlenberg

Alon Naveh

Jasmin Ajanovic

Blaise B Fanning

Ioannis T Schoinas

Daniel Cutter

John H Feit

Inder M Sodhi

Dheeraj R Subbareddy

Eugene Gorbatov

Scott P Dubal

Russell Jerome Fenger

Jianping Zhou

David A Koufaty

Mingqiu Sun

Ganapati Srinivasa

Mesut A Ergin

Meng Shi

Abhishek R Appu

Joydeep Ray

Altug Koker

Elmoustapha Ould-Ahmed-Vall

David M Durham

Ravi L Sahita

Mohan J Kumar

Varghese George

Rajesh M Sankaran

Simon C Steely, Jr

Martin Guy Dixon

Ren Wang

Baiju V Patel

Patrick L Connor

Hari Kannan

Sujoy Sen

Raanan Sade

Shih-Lien L Lu

Michael Lemay

Eriko Nurvitadhi

Eric C Samson

Sara S Baghsorkhi

Ben J Ashbaugh

Debra Bernstein

Alexander W Min

Avinash N Ananthakrishnan

Scott R Janus

John Joseph Browne

Yen-Cheng Liu

Jr-Shian Tsai

Quinn Able Jacobson

Susanne M Balle

Pete D Vogt

Lakshman Krishnamurthy

Menachem Adelman

Mike MacPherson

Vadim Sukhomlinov

Pattabhiraman K

Josh B Mastronarde

Alexander Bachmutsky

Tryggve Fossum

Robert S Chappell

Stephen R Van Doren

Stephen T Palermo

Ian M Steiner

William C Hasenplaugh

Ashish V Choubal

Antonio Gonzalez

Fernando Latorre

Nagasubramanian Gurumoorthy

Michael E Deisher

Rupin H Vakharwala

Eric Dahlen

Various systems and methods for implementing computational storage are described herein. An orchestrator system is configured to: receive, at the orchestrator system, a registration package, the registration package including function code, a logical location of input data for the function code, and an event trigger for the function code, the event trigger set to trigger in response to when the input data is modified; interface with a storage service, the storage service to monitor the logical location of the input data and notify a location service when the input data is modified; interface with the location service to obtain a physical location of the input data, the location service to resolve the physical location from the logical location of the input data; and configure the function code to execute near the input data.

Computational storage in a function-as-a-service architecture

Methods, apparatus, systems, and articles of manufacture to perform weight and activation compression and decompression are disclosed. An example apparatus includes memory, instructions in the apparatus, and processor circuitry to execute the instructions to execute a compression operation to obtain compressed data corresponding to weights in a weight matrix, and determine meta-data associated with the weight matrix, a first portion of the meta-data indicative of whether the weight matrix is compressed, a second portion of the meta-data indicative of a cache size of the compressed data, and a third portion of the meta-data indicative of the compression operation executed to obtain the compressed data.

Methods and apparatus to perform weight and activation compression and decompression

An apparatus to facilitate inferred object shading is disclosed. The apparatus comprises one or more processors to receive rasterized pixel data and hierarchical data associated with one or more objects and perform an inferred shading operation on the rasterized pixel data, including using one or more trained neural networks to perform texture and lighting on the rasterized pixel data to generate a pixel output, wherein the one or more trained neural networks uses the hierarchical data to learn a three-dimensional (3D) geometry, latent space and representation of the one or more objects.

Inferred shading mechanism

An apparatus and method for closed loop dynamic resource allocation. For example, one embodiment of a method comprises: collecting data related to usage of a plurality of resources by a plurality of workloads over one or more time periods, the workloads including priority workloads associated with one or more guaranteed performance levels and best effort workloads not associated with guaranteed performance levels; analyzing the data to identify resource reallocations from one or more of the priority workloads to one or more of the best effort workloads in one or more subsequent time periods while still maintaining the guaranteed performance levels; reallocating the resources from the priority workloads to the best effort workloads for the subsequent time periods; monitoring execution of the priority workloads with respect to the guaranteed performance level during the subsequent time periods; and preemptively reallocating resources from the best effort workloads to the priority workloads during the subsequent time periods to ensure compliance with the guaranteed performance level and responsive to detecting that the guaranteed performance level is in danger of being breached.

Apparatus and method for a closed-loop dynamic resource allocation control framework

Systems, methods, and apparatuses for resource bandwidth monitoring and control are described. For example, in some embodiments, an apparatus comprising a requestor device to send a credit based request, a receiver device to receive and consume the credit based request, and a delay element in a return path between the requestor and receiver devices, the delay element to delay a credit based response from the receiver to the requestor are detailed.

Systems, apparatuses, and methods for resource bandwidth enforcement

Methods, apparatus, systems, and articles of manufacture are disclosed to optimize resources in edge networks. An example apparatus includes agent managing circuitry to invoke an exploration agent to identify platform resource devices, select a first one of the identified platform resource devices, and generate first optimization metrics for the workload corresponding to the first one of the identified platform resource devices, the first optimization metrics corresponding to a first path. The example agent is to further select a second one of the identified platform resource devices, generate second optimization metrics for the workload corresponding to the second one of the identified platform resource devices, the second optimization metrics corresponding to a second path. The example apparatus also includes benchmark managing circuitry to embed second semantic information to the workload, the second semantic information including optimized graph information and platform structure information corresponding to the second one of the identified platform resource devices, and reconfiguration managing circuitry to select the first path or the second path during runtime based on (a) service level agreement (SLA) information and (b) utilization information corresponding to the first and second identified platform resource devices.

Methods, systems, articles of manufacture and apparatus to optimize resources in edge networks

Embodiments described herein include software, firmware, and hardware that provides techniques to enable deterministic scheduling across multiple general-purpose graphics processing units. One embodiment provides a multi-GPU architecture with uniform latency. One embodiment provides techniques to distribute memory output based on memory chip thermals. One embodiment provides techniques to enable thermally aware workload scheduling. One embodiment provides techniques to enable end to end contracts for workload scheduling on multiple GPUs.

Graphics processor operation scheduling for deterministic latency

An apparatus and method for dynamic resource allocation with mile/performance markers. For example, one embodiment of a processor comprises: resource allocation circuitry to allocate a plurality of hardware resources to a plurality of workloads including priority workloads associated with one or more guaranteed performance levels; and monitoring circuitry to evaluate execution progress of a workload across a plurality of nodes, each node to execute one or more processing stages of the workload, wherein the monitoring circuitry is to evaluate the execution progress of the workload, at least in part, by reading progress markers advertised by the workload at the specified processing stages, wherein the monitoring circuitry is to detect that the workload may not meet one of the guaranteed performance levels based on the progress markers, and wherein the resource allocation circuitry, responsive to the monitoring circuitry, is to reallocate one or more of the plurality of hardware resources to improve the performance level of the workload.

Apparatus and method for a resource allocation control framework using performance markers

Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution. Furthermore, the computing system enables customers to pay only when their code gets executed with a granular billing down to millisecond increments.

Function as a service (FaaS) system enhancements

Various embodiments for dynamically generating an advertisement in a video stream are disclosed. In one embodiment, video stream content associated with a video stream for a user device is received. Video analytics data is obtained for the video stream content, which indicates a scene recognized in the video stream content. An advertisement to be generated and inserted into the video stream content is then selected based on the scene recognized in the video stream content, and an advertisement template for generating the selected advertisement is obtained. Video advertisement content corresponding to the advertisement is then generated based on the advertisement template and the video analytics data. The video advertisement content is then inserted into the video stream content, and the modified video stream content is transmitted to the user device.

On-demand generation and personalization of video content

An apparatus and method for scheduling threads on local and remote processing resources. For example, one embodiment of an apparatus comprises: a local graphics processor to execute threads of an application; graphics processor virtualization circuitry and/or logic to generate a virtualized representation of a local processor; a scheduler to identify a first subset of the threads for execution on a local graphics processor and a second subset of the threads for execution on a virtualized representation of a local processor; the scheduler to schedule the first subset of threads on the local graphics processor and the second subset of the threads by transmitting the threads or a representation thereof to Cloud-based processing resources associated with the virtualized representation of the local processor; and the local graphics processor to combine first results of executing the first subset of threads on the local graphics processor with second results of executing the second subset of threads on the Cloud-based processing resources to render an image frame.

Apparatus and method for real time graphics processing using local and cloud-based graphics processing resources

Systems, methods, and apparatuses for resource monitoring identification reuse are described. In an embodiment, a system comprising a hardware processor core to execute instructions storage for a resource monitoring identification (RMID) recycling instructions to be executed by a hardware processor core, a logical processor to execute on the hardware processor core, the logical processor including associated storage for a RMID and state, are described.

Systems, methods, and apparatuses for resource monitoring

Methods, systems, articles of manufacture and apparatus are disclosed to improve autonomous machine capabilities. An example disclosed apparatus includes an agent task manager to retrieve native sensor input data from a sensor of the agent, an agent characteristics engine to identify environmental characteristics based on the retrieved native sensor input data, and a resource allocation modifier to allocate a first quantity of resources of the agent based on a likelihood score associated with the environmental characteristics.

Methods, systems, articles of manufacture and apparatus to improve autonomous machine capabilities

Growing community of inventors

Portland, OR, United States of America

Ravishankar Iyer