Intel Corporation

Mcafee, Inc.

Other

Musarubra US LLC

Imtel Corporation

Rajesh Poornachandran

Ned M Smith

Vincent J Zimmer

Gyan Prakash

Saurabh Dadu

Francesc Guim Bernat

Gopinatth Selvaraje

Kshitij Arun Doshi

Rita H Wouhaybi

Mingqiu Sun

Marcos Emanuel Carranza

Karthik Kumar

Karthik Veeramani

Rajneesh Chowdhury

Selim Aissi

Karunakara Kotary

Kapil Sood

Igor G Muttik

Uttam K Sengupta

Jiewen Yao

Nithyananda Siva Jeganathan

Glen J Anderson

John Joseph Browne

Lakshman Krishnamurthy

Tarun Viswanathan

Michael D Rosenzweig

Joydeep Ray

Elmoustapha Ould-Ahmed-Vall

Prasoonkumar Surti

Ben J Ashbaugh

Simon R Hunt

Omesh Tickoo

Chandrasekaran Sakthivel

Mats Gustav Agerstam

Michael J Nolan

Oguz Hasan Elibol

John Charles Weast

Ravishankar Iyer

Mark Angus MacDonald

Sven Schrecker

Sarathy Jayakumar

Prakash N Iyer

Rajeev D Muralidhar

Venkatesh Ramamurthy

Swarnendu Kar

Nilesh K Jain

Jill MacDonald Boyce

Mohan J Kumar

Paul S Diefenbaugh

Prashant Dewan

Thomas Willhalm

Mark Y Kelly

Farid Adrangi

Keith Nolan

Timothy Verrall

Gregory Burns

Shahrokh Shahidzadeh

Alexander Bachmutsky

Nathan Heldt-Sheller

William C Deleeuw

Stanley T Mo

Dario Sabella

Patrick G Kutch

Cedric Cochin

Nagasubramanian Gurumoorthy

John Brady

Zheng Zhang

Peter J Barry

Maarten Koning

Manoj R Sastry

Reshma Lal

Hormuzd M Khosravi

Barry E Huntley

Jesse R Walker

Hong C Li

Alex Nayshtut

Giuseppe Beppe Raffa

Carl S Marshall

Eriko Nurvitadhi

Ignacio Javier Alvarez

Srikathyayani Srikanteswara

Robert Charles Swanson

Robert William Wisniewski

Pradeep M Pappachan

Wei Ey Li

Igor Tatourian

Eugene Gorbatov

Omer Ben-Shalom

Selvakumar Panneer

Katalin Klara Bartfai-Walcott

Sanjay Bakshi

Saurabh Gupta

John S Howard

Joel Robert Spurlock

Gunner D Danneels

Mona Vij

Rameshkumar G Illikkal

Ravikumar Balakrishnan

Brian Kenneth Vogel

Nicholas J Adams

Methods, apparatuses and system provide for technology that interleaves a plurality of verification commands with a plurality of copy commands in a command buffer, wherein each copy command includes a message authentication code (MAC) derived from a master session key, wherein one or more of the plurality of verification commands corresponds to a copy command in the plurality of copy commands, and wherein a verification command at an end of the command buffer corresponds to contents of the command buffer. The technology may also add a MAC generation command to the command buffer, wherein the MAC generation command references an address of a compute result.

Graphics security with synergistic encryption, content-based and resource management technology

Methods, apparatus, systems and articles of manufacture to dynamically control devices based on distributed data are disclosed. An example apparatus includes a comparator to compare a first measurement measured by a first peer device to a second measurement, the second measurement being measured locally by the apparatus; and an operation adjuster to, when the comparison satisfies a threshold, adjust a measurement protocol of the first peer device.

Methods and apparatus to dynamically control devices based on distributed data

According to various aspects, controller for an automated machine may include: a processor configured to: compare information about a function of the automated machine with information of a set of tasks available to a plurality of automated machines; negotiate, with the other automated machines of the plurality of automated machines and based on a result of the comparison, which task of the set of tasks is allocated to the automated machine.

Automated machine collaboration

Embodiments described herein are generally directed to the use of sidecars to perform dynamic Application Programming Interface (API) contract generation and conversion. In an example, a first sidecar of a source microservice intercepts a first call to a first API exposed by a destination microservice. The first call makes use of a first API technology specified by a first contract and is originated by the source microservice. An API technology is selected from multiple API technologies. The selected API technology is determined to be different than the first API technology. Based on the first contract, a second contract is dynamically generated that specifies an intermediate API that makes use of the selected API technology. A second sidecar of the destination microservice is caused to generate the intermediate API and connect the intermediate API to the first API.

Dynamic application programming interface (API) contract generation and conversion through microservice sidecars

Methods, apparatus, systems, and articles of manufacture are disclosed to relocate a compute thread, the apparatus comprising control circuitry to maintain a location of a plurality of domain access counters associated with a plurality of compute-memory domains for a first compute thread, and an execution monitor to set a first domain access counter of the plurality of domain access counters, the first domain access counter associated with a first compute-memory domain of the compute-memory domains, and relocate the first compute thread to a second compute-memory domain of the compute-memory domains in response to a comparison between a second domain access counter associated with the second compute-memory domain and the first domain access counter.

Methods and apparatus for task relocation based on memory access patterns

A device including a processor configured to detect an environment of an automated machine, wherein the environment comprises one or more further automated machines; determine an action taken by the one or more further automated machines; determine an action expected of the one or more further automated machines; compares the taken action with the expected action; determine an accuracy score associated with the one or more further automated machines based on the comparison.

Autonomous machine collaboration

Methods and apparatus for Time-Sensitive Networking Coordinated Transfer Learning in industrial settings are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to cause performance of an operation by a first machine according to a first configuration, process a performance metric of the performance of the operation by the first machine to determine whether the performance metric is within a threshold range, and in response to a determination that the performance metric is not within the threshold range, cause performance of the operation by a second machine according to a second configuration different from the first configuration.

Methods and apparatus for time-sensitive networking coordinated transfer learning in industrial settings

The Internet can be configured to provide communications to a large number of Internet-of-Things (IoT) devices. Devices can be designed to address the need for network layers, from central servers, through gateways, down to edge devices, to grow unhindered, to discover and make accessible connected resources, and to support the ability to hide and compartmentalize connected resources. Network protocols can be part of the fabric supporting human accessible services that operate regardless of location, time, or space. Innovations can include service delivery and associated infrastructure, such as hardware and software. Services may be provided in accordance with specified Quality of Service (QoS) terms. The use of IoT devices and networks can be included in a heterogeneous network of connectivity including wired and wireless technologies.

Internet of things

Methods, apparatus, and articles of manufacture to conditionally activate a big core in a computing system are disclosed. An example apparatus including instructions stored in the apparatus; and processor circuitry to execute the instructions to: in response to a request to operate two or more processing devices as a single processing device, determine whether the two or more processing devices are available and capable of executing instructions according to the request; when the two or more processing devices are available and capable: split the instructions into first sub-instructions and second sub-instructions; provide (a) the first sub-instructions to a first processing device of the two or more processing devices and (b) the second sub-instructions to a second processing device of the two or more processing devices; and generate an output by combining a first output of the first processing device and a second output of the second processing device.

Methods and apparatus to conditionally activate a big core in a computing system

A controller for an automated machine may include including: one or more processors configured to: determine that a group affiliation of the automated machine switched from a first group of automated machines to a second group of automated machines, the first group of automated machines being assigned to one or more first tasks, the second group of automated machines being assigned to one or more second tasks; generate a message for one or more network devices of the second group of automated machines in accordance with a communication protocol, the message including information about a task performing model of the automated machine, the task performing model being based on a result of performing at least one task of the one or more first tasks by the automated machine.

Autonomous machine knowledge transfer

Various aspects are related to a map representation system including one or more processors configured to: obtain operation task data, the operation task data indicating one or more operation tasks associated with an operation of one or more autonomous agents within a corresponding operating space; and obtain operation task specific map data as a function of the operation task data, the operation task specific map data representing the operating space corresponding to the one or more operation tasks.

Map representation system and method for operating an autonomous agent

A controller including a processor configured to obtain a message from a task performing agent of a group of task performing agents allocated to a plurality of tasks, wherein the message comprises information about one or more assessments of the task performing agent, wherein the one or more assessments are based on a sensing process performed by one or more sensors of the task performing agent, wherein the task performing agent is an autonomous machine or a human agent equipped with sensors; and allocate a task of the plurality of tasks to the task performing agent, based on the information and based on whether the task performing agent is an autonomous machine or a human agent.

According to various aspects, a controller for an automated machine may include: one or more processors configured to: obtain a message from a further automated machine in accordance with a communication protocol, the message including a first result of a first sensing process that the further automated machine performs; and determine an assessment of the automated machine based on the first result and based on a second result of a second sensing process that the automated machine performs.

Automated machines and systems

Embodiments described herein are generally directed to assigning virtual machine (VM) workloads to groupings/partitions of accelerator resources. In an example, a processing resource of a host system maintains: (i) a resource data structure containing resource utilization information for each of one or more accelerators associated with the host system; and (ii) a group data structure containing information regarding each group of multiple groups of one or more virtual functions (VFs) of the one or more accelerators that has been assigned for use by a respective VM of multiple VMs running on a virtual machine monitor (VMM) of the processing resource. A request to deploy a workload associated with a first VM is received. Responsive to the request, the workload is assigned to a VF of a group of the multiple groups determined to have resource capacity available to satisfy expected resource utilization of the workload.

Dynamic routing of workloads to accelerator resources

A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.

Misuse index for explainable artificial intelligence in computing environments

Growing community of inventors

Portland, OR, United States of America

Rajesh Poornachandran