Abstract: A system on chip (102) comprising a plurality of logically homogeneous processor cores (104), each processor core comprising processing circuitry (210) to execute tasks allocated to that processor core, and task scheduling circuitry (202) configured to allocate tasks to the plurality of processor cores. The task scheduling circuitry is configured, for a given task to be allocated, to determine, based on at least one physical circuit implementation property associated with a given processor core, whether the given task is allocated to the given processor core.

Abstract: There is provided a neural processing unit for calculating an attention matrix during machine learning inference. The neural processing unit is configured to calculate: a first score matrix based on differences between a query matrix and a key matrix; a second score matrix based on differences between the key matrix and a learned key matrix; a similarity matrix based on a combination of the first score matrix and second score matrix; and an attention matrix comprising applying a normalisation function to the similarity matrix. Also provided is an apparatus comprising at least one said neural processing unit and at least one memory, the memory configured to pass, on demand, a learned key matrix to the neural processing unit. Also provided is a computer program product having computer readable program code stored thereon which, when executed by said neural processing unit, causes the unit to perform said calculations.

Abstract: A computer-implemented method of optimising a student neural network (SNN), based on a previously-trained neural network (PTNN) trained on first data (FD) using a first processing system (FPS). The method includes using a second processing system (SPS) to generate reference output data (ROD) from the previously-trained neural network (PTNN) in response to inputting second data (SD) to the previously-trained neural network (PTNN). The method also includes optimising a student neural network (SNN) for processing the second data (SD) with the second processing system (SPS), by using the second processing system (SPS) to adjust a plurality of parameters of the student neural network (SNN) such that a difference (DIFF) between the reference output data (ROD), and second output data (SOD) generated by the student neural network (SNN) in response to inputting the second data (SD) to the student neural network (SNN), satisfies a stopping criterion.

Abstract: Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to adapt a computing device to classify physical features in a deployment environment. In a particular implementation, computing resources may be selectively de-allocated from at least one of one or more elements of a computing architecture based, at least in part, on assessed impacts to the one or more elements of the computing architecture.

Abstract: The present disclosure provides a catheter assembly including a catheter tube and an expandable unit attached to the catheter tube. The expandable unit is configurable in a collapsed configuration for facilitating delivery of the catheter assembly through a body lumen and a fully expanded configuration for contacting an interior wall of the body lumen to cover a perforation. The expandable unit defines a longitudinal channel extending from a proximal end to a distal end of the expandable unit when in the fully expanded configuration, permitting blood flow past the expandable unit. The disclosure further provides a method of treating a perforation by delivering a catheter assembly with an expandable unit to a perforation site, expanding the expandable unit to contact an interior wall of a body lumen to cover the perforation, and permitting blood flow past the expandable unit while in contact with the interior wall covering the perforation.

Abstract: A method of operating a system having a plurality of neural networks includes receiving sequential input data events and processing each sequential input data event using a corresponding subset of the plurality of neural networks to obtain a plurality of sequential outputs. Each sequential output is indicative of a predictive determination of an aspect of the corresponding input data event. The method includes processing the plurality of sequential outputs to determine an uncertainty value associated with the plurality of sequential outputs, and operating the system based on the determined uncertainty value.

Abstract: A computer-implemented method of providing a group of neural networks for processing data includes: identifying a group of neural networks including a main neural network and one or more sub-neural networks, each neural network comprising a plurality of parameters and wherein one or more of the parameters of each sub-neural network are shared by the sub-neural network and the main neural network; inputting training data into each neural network, and adjusting the parameters of each neural network; computing a performance score for each neural network using the adjusted parameters; generating a combined score for the group of neural networks by combining the performance score, with a value of a loss function computed for each neural network using the adjusted parameters; repeating the identifying and the inputting and the adjusting and the computing and the generating; and selecting a group of neural networks for processing data in the plurality of hardware environments based on the value of the combined score

Abstract: A computer-implemented method of identifying a neural network for processing data includes: clustering a training dataset into a plurality of data clusters based on similarities in activation patterns generated in neurons of a teacher neural network in response to inputting the training dataset into the teacher neural network, training a student neural network for processing each of the plurality of data clusters, and providing a data classifier neural network for identifying one or more of the trained student neural networks to process data based on a data cluster of the data.