Huawei Technologies Co., Limited

International Business Machines Corporation

Evertz Microsystems Ltd.

Barnaby Dalton

Manuel Saldana

Vanessa Courville

Ettore Tiotto

Ilie G Tanase

Gheorghe Almasi

Zahra Lak

Kai-Ting Amy Wang

Robert J Blainey

Jeff Wei

Brian Moody

Jeremy Blythe

Louis Ly

Serdar Sozubek

James A Sedgwick

Lior Velichover

Rakesh Thakor Patel

Graham Taylor

Sepideh Kharaghani

Andrew Duncan

Robert Beattie

A system and method for content aware monitoring of an output of a media channel by a media system is provided herein. In at least one embodiment, the method comprises: receiving a media assertion schedule comprising a schedule of assertion checks which allow validating that the output of the media channel, by the media system, is synchronized with an expected media channel output; receiving, from a signature generating module, at least one observed signature file; determining that the timestamp data, in each of the at least one received observed signature file, aligns with at least one timecode included in an assertion check in the media assertion schedule; identifying an assertion condition included in the assertion check; and validating the assertion condition using the observed media frame signatures included in each of the at least one received observed signature file.

Method and system for content aware monitoring of media channel output by a media system

Systems and methods are provided for processing data streams. The system includes at least one data source for transmitting a data stream to a data transmission network; at least one specific purpose processor in communication with the data transmission network, wherein the specific purpose processor is configured to provide a specific data processing operation; a controller coupled the data transmission network, the controller configured to: determine that the data stream requires processing according to a data processing operation; identify a data processing configuration corresponding to the data processing operation; and route the data stream to the at least one specific purpose processor.

Systems and methods for processing data streams

Allocating distributed data structures and managing allocation of a symmetric heap can include defining, using a processor, the symmetric heap. The symmetric heap includes a symmetric partition for each process of a partitioned global address space (PGAS) system. Each symmetric partition of the symmetric heap begins at a same starting virtual memory address and has a same global symmetric break. One process of a plurality of processes of the PGAS system is configured as an allocator process that controls allocation of blocks of memory for each symmetric partition of the symmetric heap. Using the processor executing the allocator process, isomorphic fragmentation among the symmetric partitions of the symmetric heap is maintained.

Allocation of distributed data structures

A circuit for a multistage sequential data process includes a plurality of memory units. Each memory unit is associated with a stage of the sequential data process which, for each data set inputted to the stage, the stage provides an intermediate data set for storage in the associated memory unit for use in at least one subsequent stage of the sequential data process, where each of the plurality of memory units is sized based on relative locations of the stage providing the intermediate data set and the at least one subsequent stage in the sequential data process.

Systems, methods and devices for a multistage sequential data process

The present disclosure is drawn to the reduction of parameters in fully connected layers of neural networks. For a layer whose output is defined by y=Wx, where y&#x2208;Ris the output vector, x&#x2208;Ris the input vector, and W&#x2208;Ris a matrix of connection parameters, matrices Uand Vare defined and submatrices Ware computed as the product of Uand V, so that W=VU, and W is obtained by appending submatrices W.

Reduction of parameters in fully connected layers of neural networks by low rank factorizations

A method for training a neural network comprising at least one layer comprising a plurality of input nodes, a plurality of output nodes, and a plurality of connections for connecting each one of the plurality of input nodes to each one of the plurality of output nodes, is provided. The method comprises pseudo-randomly selecting a subset of the plurality of connections, each connection of the plurality of connections having associated therewith a weight parameter and a probability of being retained in the neural network, generating output data by feeding input data over the subset of connections, computing an error between the generated output data and desired output data, and, for at least one connection in the subset of connections, determining a contribution of the weight parameter to the error and updating the probability of being retained in the neural network accordingly.

System and method for training a neural network

A memory control unit for handling data stored in a memory device includes a first interface to an interconnection with at least one memory bank; a second interface for communicating with a data requesting unit; and a memory quantization unit. The memory quantization unit is configured to: obtain, via the first interface, a first weight value from the at least one memory bank; quantize the first weight value to generate at least one quantized weight value having a shorter bit length than a bit length of the first weight value; and communicate the at least one quantized weight value to the data requesting unit via the second interface.

Systems, methods and devices for data quantization

The present disclosure is drawn to the reduction of parameters in fully connected layers of neural networks. For a layer whose output is defined by y=Wx, where y is the output vector, x is the input vector, and W is a matrix of connection parameters, vectors uand vare defined and submatrices Ware computed as the outer product of uand v, so that W=v&#x2297;u, and W is obtained by appending submatrices W.

Reduction of parameters in fully connected layers of neural networks

A computing system includes at least one processor and at least one module operable by the at least one processor to calculate a tail of a first dataset by determining elements of the first dataset that fall outside of a specified percentile, and determine locations of the first dataset at which elements of the first dataset that fall outside of the specified percentile are located. The at least one module may be operable to calculate a tail of a second dataset by populating a data structure with elements of the second dataset that correspond to the locations of the first dataset, and determining, using the data structure, elements of the second dataset that fall outside of the specified percentile. The at least one module may be operable to output an indication of at least one of the tail of the first dataset or the tail of the second dataset.

Efficient tail calculation to exploit data correlation

Systems, devices and methods for data compression using history search for dictionary based compression. Systems, devices and methods may use parallel processing techniques for data compression and encoding. Systems, devices and methods may provide memory search techniques for hardware.

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Mississauga, Canada

Barnaby Dalton