Intel Corporation

Nilesh K Jain

Ravishankar Iyer

Rajesh Poornachandran

Selvakumar Panneer

Ned M Smith

Francesc Guim Bernat

Jill MacDonald Boyce

Kshitij Arun Doshi

Varghese George

Andrew J Herdrich

Carl S Marshall

Prasoonkumar Surti

Glen J Anderson

Adam T Lake

Abhishek R Appu

Joydeep Ray

Altug Koker

Elmoustapha Ould-Ahmed-Vall

Robert Valentine

David M Durham

Ravi L Sahita

Mark Jay Charney

Zeev Sperber

Scott R Janus

Jr-Shian Tsai

Deepak Shashidhar Vembar

Karthik Veeramani

Menachem Adelman

Mohan J Kumar

Christopher J Hughes

Karthik Kumar

Rajesh M Sankaran

Kapil Sood

Amit Gradstein

Ren Wang

Baiju V Patel

Alexander F Heinecke

Jesse R Walker

Thomas Willhalm

Simon Rubanovich

Raanan Sade

Michael Lemay

Eriko Nurvitadhi

Eric C Samson

Sara S Baghsorkhi

Ben J Ashbaugh

Timothy Verrall

Suraj Prabhakaran

James M Holland

Sanjay Bakshi

Mike MacPherson

Vadim Sukhomlinov

Dan Baum

Brinda Ganesh

Saurabh Tangri

Luis S Kida

Soethiha Soe

Trevor Cooper

Alexander Vul

Pattabhiraman K

Josh B Mastronarde

Alexander Bachmutsky

Mingqiu Sun

Evan Custodio

Naveen K Mellempudi

Ian M Steiner

Sameh Gobriel

Mona Vij

Narayan Ranganathan

Edwin Verplanke

Patrick G Kutch

Rameshkumar G Illikkal

Jeffrey D Chamberlain

Theodore L Willke

Eric Dahlen

Sumit Mohan

Rinat Rappoport

Mohammad R Haghighat

Yipeng Wang

Zhijun Lei

Ramanathan Sethuraman

Somnath Paul

Mingwei Zhang

Evangelos Georganas

Amin Firoozshahian

Barukh Ziv

Marcin Spoczynski

Rashmin N Patel

Vinay Phegade

Faouzi Kossentini

Yeluri Raghuram

Arik Narkis

Monica Kenguva

Dmitry Babokin

Sebastian Possos

Mrittika Ganguli

Adrian C Hoban

Anil Rao

Ravi Iyer

Kushal Datta

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

Systems, methods, and apparatuses relating sparsity based FMA. In some examples, an instance of a single FMA instruction has one or more fields for an opcode, one or more fields to identify a source/destination matrix operand, one or more fields to identify a first plurality of source matrix operands, one or more fields to identify a second plurality of matrix operands, wherein the opcode is to indicate that execution circuitry is to select a proper subset of FP8 data elements from the first plurality of source matrix operands based on sparsity controls from a first matrix operand of the second plurality of matrix operands and perform a FMA.

Instructions for structured-sparse tile matrix FMA

A recommendation system includes a recommendation model for generating a recommendation score for a user with respect to an item. The model is configured to receive a set of dense features, describing numerical information, and a set of sparse features, representing a subset of items from a relatively large group of items. To represent the subset of items in the sparse features, each item (or a symbol thereof) is processed by an encoder to represent each item with a plurality of positions in a sparse binary representation of the subset of items. The sparse binary representation is then processed by a model that determines a vector representation of the sparse category features used in the prediction in conjunction with the dense features.

Recommendation system with sparse feature encoding

Examples described herein relate to offload circuitry comprising one or more compute engines that are configurable to perform a workload offloaded from a process executed by a processor based on a descriptor particular to the workload. In some examples, the offload circuitry is configurable to perform the workload, among multiple different workloads. In some examples, the multiple different workloads include one or more of: data transformation (DT) for data format conversion, Locality Sensitive Hashing (LSH) for neural network (NN), similarity search, sparse general matrix-matrix multiplication (SpGEMM) acceleration of hash based sparse matrix multiplication, data encode, data decode, or embedding lookup.

Hardware offload circuitry

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

Example methods, apparatus, and systems to manage quality of service with respect to service level agreements in a computing device are disclosed. An example apparatus includes a first mesh proxy assigned to a first platform-agnostic application, the first mesh proxy to generate a first resource request signal based on a first service level agreement requirement from the first platform-agnostic application; a second mesh proxy assigned to a second platform-agnostic application, the second mesh proxy to generate a second resource request signal based on a second service level agreement requirement from second platform-agnostic application; and a load balancer to allocate hardware resources for the first platform-agnostic application and the second platform-agnostic application based on the first resource request signal and the second resource request signal.

Methods and apparatus to manage quality of service with respect to service level agreements in a computing device

Techniques related to distributing the video encoding processing of an input video across hardware and software systems. Such techniques include evaluating the content of the video and determine whether or the encoding operation is best to be done on the hardware system only, software system only or a hybrid hardware and software system.

Offloading video coding processes to hardware for better density-quality tradeoffs

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

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

Methods, apparatus, systems and articles of manufacture to determine provenance for data supply chains are disclosed. Example instructions cause a machine to at least, in response to data being generated, generate a local data object and object metadata corresponding to the data; hash the local data object; generate a hash of a label of the local data object; generate a hierarchical data structure for the data including the hash of the local data object and the hash of the label of the local data object; generate a data supply chain object including the hierarchical data structure; and transmit the data and the data supply chain object to a device that requested access to the data.

Methods and apparatus to determine provenance for data supply chains

Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.

Neural network processing for multi-object 3D modeling

Methods, apparatus, and system determine if a data class in a plurality of data classes is separable, such as by determining an average intra-class similarity within each data class, inter-class similarity across all data classes in the plurality of data classes, and determining separability based on the average intra-class similarity relative to the inter-class similarity. Data classes determined to be highly variable may be removed. Pair(s) of data classes not separable from one another may be combined into one class or one of the data classes may be dropped. A hardware accelerator, which may comprise artificial neurons, accelerate performance of the data analysis.

Data class analysis method and apparatus

Example systems, methods, and apparatus to generate optimized models for Internet of Things device are disclosed. An example apparatus includes a data receiver to collect data from a sensor of an internet of things device based a first sampling frequency and a buffer having a first buffer size; a model trainer to train a model based on the data collected from the sensor; a buffer analyzer to select a second sampling frequency and to reduce the buffer to a second buffer size, the model trainer to update the model based on the second buffer size; and a platform analyzer to: determine a duration of time that that internet of things device will take to analyze sensor data based on the updated model.

Methods and apparatus to generate optimized models for internet of things devices

Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.

Technologies for platform-targeted machine learning

Growing community of inventors

Beaverton, OR, United States of America

Nilesh K Jain