Intel Corporation

Jeffrey R Jackson

Glen J Anderson

Tsung-Yuan Charles Tai

Richard A Uhlig

Feng Chen

Christian Maciocco

Carl S Marshall

Xiaoming Chen

Rajkishore Barik

Brian T Lewis

Murali Sundaresan

Rajendra S Yavatkar

Ashok Sunder Rajan

Daniel J Dahle

Prasoonkumar Surti

Justin E Gottschlich

Selvakumar Panneer

Mike MacPherson

Mike B Macpherson

John W Sherry

Daniel S Lake

Jose K Sia, Jr

Andrea E Johnson

Dawn Nafus

Heather Patterson

Sanjay Kumar

Dheeraj R Subbareddy

Stanley T Mo

Christopher J Cormack

Scott B Blum

Sanjay K Kumar

Technologies for computing context replay include a computing device having a persistent memory and a volatile memory. The computing device creates multiple snapshots that are each indicative of a user's computing context at a corresponding sync point. The snapshots may include metadata created in response to system events, memory snapshots stored in a virtual machine, and/or video data corresponding to the computing context. At least a part of the snapshots are stored in the persistent memory. The computing device presents a timeline user interface based on the snapshots. The timeline includes multiple elements that are associated with corresponding sync points. The timeline elements may visually indicate a salience value that has been determined for each corresponding sync point. In response to a user selection of a sync point, the computing device activates a computing context corresponding to the snapshot for the selected sync point. Other embodiments are described and claimed.

Technologies for computing context replay with visual searching

A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine an optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.

Tool for facilitating efficiency in machine learning

In the present disclosure, functions associated with the central office of an evolved packet core network are co-located onto a computer platform or sub-components through virtualized function instances. This reduces and/or eliminates the physical interfaces between equipment and permits functional operation of the evolved packet core to occur at a network edge.

Cross-function virtualization of a telecom core network

Apparatuses, methods, and storage medium associated with a browser for prioritized display of videos and/or photographs are disclosed herein. In embodiments, an apparatus may include one or more processors, devices, and/or circuitry to operate a browser to present a plurality of photos and/or videos for viewing. A subset of the plurality of the photos and/or videos may be selected based on the results of an analysis of sensor data collected by a plurality of wearable sensors. The subset of the plurality of the photos and/or videos may be prioritized over other photos and/or videos from the plurality of the photos and/or videos in terms of presentation space allocated for presentation.

Prioritization for presentation of media based on sensor data collected by wearable sensor devices

A mechanism is described for facilitating smart collection of data and smart management of autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and combining a first computation directed to be performed locally at a local computing device with a second computation directed to be performed remotely at a remote computing device in communication with the local computing device over the one or more networks, where the first computation consumes low power, wherein the second computation consumes high power.

Smart autonomous machines utilizing cloud, error corrections, and predictions

A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.

In one embodiment, an autonomous vehicle (AV) system determines that a collision between itself and another object is imminent based on data obtained from sensors attached to the AV. The AV system determines an optimal vehicle orientation for the collision, determines a vehicle path to position the vehicle in the optimal vehicle orientation, and autonomously controls the vehicle to travel along the determined vehicle path.

Proactive vehicle safety system

Autonomous machines through cloud, error corrections, and predictions

In one embodiment, an apparatus comprises a memory to store executable instructions of an operating system and a processor to identify a request for data from an application; determine whether a persistent page cache stores a copy of the data, wherein the persistent page cache is directly addressable by the processor and is to cache data of a storage device that is not directly addressable by the processor; and access the data from the persistent page cache.

Method and apparatus for persistently caching storage data in a page cache

Response time is improved by a method and apparatus for pre-fetching application data over a network. A request for application data, such as Internet data, is received. The request is transmitted to a transparent network proxy, which issues requests over a communications network to fetch the requested data. The requested application data are received by the proxy and forwarded to a client layered service provider, which supplies the data to the application. If additional application data is received by the proxy, that data is also forwarded to the client layered service provider, where it is stored as pre-fetch data. If additional application data is requested by the application, the additional data can be supplied from the stored pre-fetch data, if available.

Method and apparatus for pre-fetching data for an application using a winsock client layered service provider and a transparent proxy

Growing community of inventors

Newberg, OR, United States of America

Jeffrey R Jackson