Intel Corporation

Other

Analog Devices,inc.

Anthony L Kelly

Andrey Chilikin

Stephen Doyle

Brendan Ryan

Krishnamurthy Jambur Sathyanarayana

Fiona Trahe

Patrick L Connor

John Joseph Browne

Chris MacNamara

Tomasz Kantecki

Ciara Loftus

Jeffrey Carl Gealow

A computing device includes an appliance status table to store at least one of reliability and performance data for one or more network functions virtualization (NFV) appliances and one or more legacy network appliances. The computing device includes a load controller to configure an Internet Protocol (IP) filter rule to select a packet for which processing of the packet is to be migrated from a selected one of the one or more legacy network appliances to a selected one of the one or more NFV appliances, and to update the appliance status table with received at least one of reliability and performance data for the one or more legacy network appliances and the one or more NFV appliances. The computing device includes a packet distributor to receive the packet, to select one of the one or more NFV appliances based at least in part on the appliance status table, and to send the packet to the selected NFV appliance. Other embodiments are described herein.

Migration from a legacy network appliance to a network function virtualization (NFV) appliance

A power system that uses machine learning algorithms to solve various problems related to the delivery of power on integrated circuit systems is provided. The power system may process data on a platform near the target integrated circuit or off-platform in a cloud so that the machine learning algorithms can extract information from the data, process and analyze the data, and perform suitable action based on the analysis results. Applying machine learning to integrated circuit power delivery may involve the application of algorithms such as anomaly detection, load prediction, regression, and classification. Operated in this way, the power system may be provided with improved voltage/frequency scaling capabilities, security, and power efficiency.

Integrated circuit power systems with machine learning capabilities

A compaction compensation system comprising a mechanism for automatically height adjusting the height adjustable mounting () so as to height adjust the sensor relative to the screed by a compaction compensating factor. It is used in a paving machine () for laying compactable paving material, the paving machine comprising: (a) a height adjustable screed () for laying a layer of compactable paving material onto a surface; (b) a sensor () for sensing the height of the screed relative to a reference level, the sensor being attached to the screed by a height adjustable mounting; (c) a screed height control system for automatically adjusting the height of the screed in response to different heights sensed by the sensor due to contours of the surface, so as to adjust the screed to lay a layer of varying thickness.

Compaction compensation system

Examples include techniques to monitor control plane (CP) network traffic. Examples include monitoring CP traffic between one or more user equipment (UEs) wirelessly coupled to a network and a virtual network function arranged to process user plane (UP) traffic for an application service provided to the one or more UEs to determine whether at least a portion of the UP traffic needs to be routed to a different VNF for UP processing.

Techniques to monitor control plane network traffic

An adaptive control system is described. The system includes a control having a plurality of control parameters, the control parameters providing for control of an associated plant. The control parameters are tuned using a prediction error filter, the prediction error filter selecting values of the control parameters that minimize the values of a prediction error between actual and predicted values of an autoregressive process.

Self compensating closed loop adaptive control system

A sample rate conversion system developed to implement a rate change of M/N using a very efficient design implementation. The sample rate conversion system of the present invention is implemented as a CIC-based interpolating sample rate converter with noise-shaped control of the N value. For a decimator, noise-shaped control of the M value is utilized. In the interpolator, the N value is the correct value on average, but demonstrates instantaneous errors (&ldquo;non-uniform&rdquo; resampling) that are corrected through noise-shaping. The CIC SRC implementation capitalizes on the fact that the outputs of the CIC that are discarded during downsampling need not be calculated by the CIC in the first instance. The combination of the computational simplicity of CIC SRC with noise-shaped, non-uniform resampling performs the sample rate conversion very economically and facilitates conversion between a plethora of sample rates at the input and output without requiring the various filters to be explicitly formulated. A method for sample rate conversion is also described.

Growing community of inventors

Dooradoyle, Ireland

Anthony L Kelly