Imagination Technologies Limited

Aria Ahmadi

Cagatay Dikici

James Imber

David Walton

Timothy Atherton

Biswarup Choudhury

Ivaxi Sheth

A method and data processing system implement a neural network containing at least one matrix multiplication operation. The matrix multiplication operation is mapped to a graph of neural network operations including at least one transformation and at least one convolution. The at least one convolution is implemented in fixed-function hardware of a neural network accelerator.

Neural network comprising matrix multiplication

A method of generating a training dataset suitable for training machine learning algorithms to estimate the motion of objects, and for training a machine learning algorithm to perform motion estimation. A plurality of pairs of synthetic images are generated from obtained objects and backgrounds, each pair have a first frame and a second frame. The first frame includes a selection of objects in first positions and first orientations superimposed on a selected background, and the second frame includes the selection of objects in second positions and second orientations superimposed on the selected background. Also provided are processing systems configured to carry out these methods.

Training a machine learning algorithm to perform motion estimation of objects in a set of frames

A hardware-implemented method of indexing data elements in a source array is provided. The method comprises generating a number of shifted copy arrays; receiving indices for indexing the source array; and retrieving one or more data elements from the shifted copy arrays, according to the received indices. Also disclosed is a related processing system comprising a memory and hardware for indexing data elements in a source array in the memory.

Indexing elements in a source array

A data processing system and method are disclosed, for implementing a windowed operation in at least three traversed dimensions. The data processing system maps the windowed operation in at least three traversed dimensions to a plurality of constituent windowed operations in two traversed dimensions. This plurality of 2-D windowed operations is implemented as such in at least one hardware accelerator. The data processing system assembles the results of the constituent 2-D windowed operations to produce the result of the windowed operation in at least three traversed dimensions.

Hardware implementation of windowed operations in three or more dimensions

A method of analysing objects in a first frame and a second frame is disclosed. The method includes segmenting the frames, and matching at least one object in the first frame with a corresponding object in the second frame. The method optionally includes estimating the motion of the at least one matched object between the frames. Also disclosed is a method of generating a training dataset suitable for training machine learning algorithms to estimate the motion of objects. Also provided are processing systems configured to carry out these methods.

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Hertfordshire, United Kingdom

Aria Ahmadi