Intel Corporation

Whirlpool Corporation

Mohammad R Haghighat

Sara S Baghsorkhi

Bin Yang

Jia Bao

Milind Baburao Girkar

Changliang Wang

David Sehr

Mingqiu Sun

Tao Xu

Yong Yao

David M Durham

Kshitij Arun Doshi

Ravi L Sahita

Mohan J Kumar

Hormuzd M Khosravi

Baiju V Patel

Ravishankar Iyer

Michael Lemay

Andrew J Herdrich

Jr-Shian Tsai

Aravindh V Anantaraman

Yong Wu

Yao Zu Dong

Vadim Sukhomlinov

Alexander Bachmutsky

Yu Wang

Zhen Zhou

John B Vicente

Ian M Steiner

Lei Li

Jeffrey D Chamberlain

Nilesh K Jain

Srinivas Sridharan

Eric Dahlen

Ajaya V Durg

Abhilasha Bhargav-Spantzel

Sebastian Winkel

Mingwei Zhang

Tianyou Li

Amin Firoozshahian

Raul Zegers Diaz

Junyong Ding

Uri Kahana

Yeluri Raghuram

Wei Hu

Dmitry Babokin

Dong-Yuan Chen

Seth Herndon

Anil Rao

Krishna Bhuyan

Leeor Peled

Mikhail E Smorkalov

Sudarshan Srinivasan

Teck Joo Goh

Areg Melik-Adamyan

Oliver W Chen

Feng W Yuan

Harshad S Sane

Chao Xie

Qiming Shi

Zhong Cao

Fengqian Gao

Xiaocheng Marc Mao

Anup Mohan

Michael Prinke

Yifei Xue

Knud J Kirkegaard

Tianmi Chen

Hongzhen Liu

Mohammad Laskar

Padmanabha C Jakkahalli

Saqib N Shamsi

Harshil Shah

Bereket Sharew

Wenyong Huang

Raul Diaz

Knud J Kirkegnard

Mikhail Averbukh

Systems, apparatuses and methods may provide for source device technology that identifies a plurality of object regions in a video frame, automatically generates context information for the video frame on a per-object region basis and embeds the context information in a signal containing the video frame. Additionally, playback device technology may decode a signal containing a video frame and embedded context information, identifies a plurality of object regions in the video frame based on the embedded context information, and automatically selects one or more post-processing configurations for the video frame on a per-object region basis.

Technology to use video source context information in post-processing operations

Systems, apparatuses and methods may provide for technology that detects a tensor operation in an application, wherein the tensor operation has an unspecified tensor input size, determines the input tensor size at runtime, and selects a partition configuration for the tensor operation based at least in part on the input tensor size and one or more runtime conditions. In one example, the technology searches a lookup table for the input tensor size and at least one of the runtime condition(s) to select the partition configuration.

Unified programming interface for regrained tile execution

Methods, apparatus, systems and articles of manufacture to deduplicate duplicate memory in a cloud-computing environment are disclosed herein. An example apparatus to deduplicate duplicate memory comprises a parser to parse process information corresponding to instances of an application, a group generator to group process information into application groups based on the process information indicating instances corresponding to the same directory paths and application names, a data structure generator to generate a pair of binary search trees for an application group, and a merge controller to deduplicate duplicate memory contents detected in the application group.

Methods and apparatus to deduplicate duplicate memory in a cloud computing environment

Controlling a heating process is provided. An image of a raw food item is captured. Using a generative model, synthesized images of the cooked food are generated at different levels of doneness based on the raw image. A selection of one of the synthesized cooked images is received. The food item is cooked to the levels of doneness corresponding to the one of the synthesized cooked images.

Generative food doneness prediction

Systems, apparatuses and methods may provide for technology that detects a generic cloud service call in an application, wherein platform-specific parameters are unspecified in the cloud service call. The technology may also select a first cloud platform based on one or more performance constraints associated with the first cloud platform and automatically generate a first platform-specific service call based on the cloud service call and the first set of parameters. In one example, the technology also maps the cloud service call to the first platform-specific service call. Additionally, the technology may migrate the cloud service call to a second cloud platform without rewriting the generic cloud service call.

Unified programming model for function as a service computing

Systems, apparatuses and methods may provide for developer stage technology that embeds binary code into an application binary file, wherein the binary code corresponds to vector functions and non-vector functions in statically typed source code, and generates intermediate representation (IR) data, wherein the intermediate representation data corresponds to the vector functions in the statically typed source code. Additionally, the developer stage technology embeds the IR data in the application binary file. Moreover, deployment stage technology may generate a first compilation output based on the application binary file and detect a capability change in an execution environment associated with the first compilation output. The deployment stage technology may also generate, in response to the detected capability change, a second compilation output based on the first compilation output.

Multi-stage automatic compilation for vector computations in applications

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

Method, systems and apparatuses may provide for technology that identifies first data and second data to be stored in a data storage. Each of the first data and the second data are in a first data format. Some technology may also interleave the first data with the second data. The interleaved first and second data are in a second data format. The second data format is different from the first data format.

Interleaved data conversion to change data formats

Systems, apparatuses and methods may provide for technology that sends a first message via an input output (IO) link, wherein the first message includes a first rendering asset and an identifier (ID) associated with the first rendering asset. The technology may also exclude a second rendering asset from a second message in response to the ID being shared by the first rendering asset and the second rendering asset and send the second message via the IO link, wherein the second message includes the ID. In one example, the ID is a hash ID.

Asset caching in cloud rendering computing architectures

Systems, apparatuses and methods may provide for technology that generates inclusion data in accordance with a Poisson distribution, wherein the inclusion data specifies a number of inclusions for each observation in a set of observations. The technology may also train a first decision tree in a random forest based at least in part on the inclusion data.

Poisson distribution based approach for bootstrap aggregation in a random forest

Systems, apparatuses and methods may provide for technology that identifies a prioritization data structure associated with a function, wherein the prioritization data structure lists hardware resource types in priority order. The technology may also allocate a first type of hardware resource to the function if the first type of hardware resource is available, wherein the first type of hardware resource has a highest priority in the prioritization data structure. Additionally, the technology may allocate, in the priority order, a second type of hardware resource to the function if the first type of hardware resource is not available.

Automated learning technology to partition computer applications for heterogeneous systems

Systems, apparatuses and methods may provide for technology that detects a runtime call to a communication library, wherein the runtime call identifies a memory buffer, determines that a class of service (CLOS) attribute is associated with the memory buffer, and issues a driver instruction to modify the CLOS attribute in response to the runtime call.

Initialization and management of class of service attributes in runtime to optimize deep learning training in distributed environments

This disclosure is directed to a context-aware proactive threat management system. In general, a device may use internal activity data along with data about external activities (e.g., provided by remote resources) for threat assessment and mitigation. A device may comprise, for example, a hostile environment detection (HED) module to coordinate threat assessment and mitigation. The HED module may accumulate internal activity data (e.g., from security services in the device), and external activity data regarding a system environment and/or a physical environment from the remote resources. The HED module may then assess threats based on the activity data and determine automated and/or manual mitigation operations to respond to the threats. In one embodiment, visualization features may also be used to, for example, visualize threats to a user, visualize automatic/manual mitigation operations, request user confirmation regarding the performance of manual mitigation operations, etc.

Context-aware proactive threat management system

A method of adding an element to a data structure may include atomically associating the element with the data structure if the element is not associated with the data structure. The element may be prepared for insertion into a location in the data structure. The method may also include atomically inserting the element into the location in the data structure if another element has not been inserted into the location.

Concurrent insertion of elements into data structures

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San Jose, CA, United States of America

Mohammad R Haghighat