Intel Corporation

Jerin C Justin

Kumar Balasubramanian

Joydeep Ray

Elmoustapha Ould-Ahmed-Vall

Prasoonkumar Surti

Rajesh Poornachandran

Ben J Ashbaugh

Chandrasekaran Sakthivel

Oguz Hasan Elibol

Anil Thomas

Jayaram Bobba

Gokce Keskin

Pradeep Ramani

Bhavani Subramanian

Rama Harihara

Jing Huang

Xiaoming Cui

Timothy B Costa

Ting Gong

Guozhong Zhuang

Naveen Manicka

Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Compression of machine learning models utilizing pseudo-labeled data training

A computing device and method for profiling and diagnostics in an Internet of Things (IoT) system, including matching an observed solution characteristic of the IoT system to an anomaly in an anomaly database.

Profiling and diagnostics for internet of things

Dynamic assignment of down sampling intervals for data stream processing

Policy-based system interface for a real-time autonomous system

A system and method for representing events that occur in a real world deployment is described. A real-world workload including multiple events is identified. Multiple characteristics of the real-world workload are converted into multiple endpoint simulator workloads. Multiple gateway hardware characteristics are converted into a modeling elements for simulated Internet of things (IoT) networks. Further, a simulation is performed for each of the endpoint simulator workloads on each of the simulated IoT networks. Also, statistics are collected about the performance of the simulated IoT networks for the endpoint simulator workloads.

Future proofing and prototyping an internet of things network

The techniques disclosed herein include a computing device for Internet of Things (IoT) solution sizing. The computing device is to determine a solution deployment metric, trigger edge traffic, monitor a round trip characteristic and an actuation pattern, execute permutations of input workloads, and determine a solution deployment.

IoT solution sizing

A method and apparatus including a computing device to quantify a term of a service agreement in a context of a proposed solution, evaluate solution characteristics against a given gateway architecture, and compare the solution characteristics to desired service-agreement solution metrics. The solution characteristics include simulated observed characteristics.

Compliance checker for service agreement

Compression in machine learning and deep learning processing

Growing community of inventors

San Jose, CA, United States of America

Jerin C Justin