Arm Limited

Apical Limited

Eric Kunze

Ian Rudolf Bratt

Jared Smolens

Lingchuan Meng

John Wakefield Brothers

Alexander Eugene Chalfin

Paul Stanley Hughes

Damian Piotr Modrzyk

Elliot Maurice Simon Rosemarine

Metin Gokhan Ünal

Jens Olson

Danny Loh

Alex Kornienko

Aaron DeBattista

Naveen Suda

Jonathan Adam Lawton

Ahmet Fatih Inci

Aspects of the present disclosure relate to apparatus comprising execution circuitry comprising at least one execution unit to execute program instructions, and control circuitry. The control circuitry receives a stream of processing instructions, and issues each received instruction to one of said at least one execution unit. Responsive to determining that a first type of context switch is to be performed from an initial context to a new context, issuing continues until a pre-emption point in the stream of processing instructions is reached. Responsive to reaching the pre-emption point, state information is stored, and the new context is switched to. Responsive to determining that a context switch is to be performed to return from the new context to the initial context, the processing status is restored from the state information, and the stream of processing instructions is continued.

Methods and apparatus for context switching

A processor arranged to compress neural network activation data comprising an input module for obtaining neural network activation data. The processor also comprises a block creation module arranged to split the neural network activation data into a plurality of blocks; and a metadata generation module for generating metadata associated with at least one of the plurality of blocks. Based on the metadata generated a selection module selects a compression scheme for each of the plurality of blocks, and a compression module for applying the selected compression scheme to the corresponding block to produce compressed neural network activation data. An output module is also provided for outputting the compressed neural network activation data.

Compression of neural network activation data

The present disclosure advantageously provides a heterogenous system, and a method for generating an artificial neural network (ANN) for a heterogenous system. The heterogenous system includes a plurality of processing units coupled to a memory configured to store an input volume. The plurality of processing units includes first and second processing units. The first processing unit includes a first processor and is configured to execute a first ANN, and the second processing unit includes a second processor and is configured to execute a second ANN. The first and second ANNs respectively include an input layer, at least one processor-optimized hidden layer and an output layer. The second ANN hidden layers are different than the first ANN hidden layers.

Specializing neural networks for heterogeneous systems

A display controllerin a data processing system includes a timewarp module (transformation circuitry)that is operable to perform timewarp processing of a rendered framegenerated by a graphics processor (GPU)for provision to a display panel. The timewarp module (transformation circuitry)operates to transform an input surfaceread by the display controllerbased on received view orientation data to provide an appropriately 'timewarped' transformed version of the input surface as an output transformed surface for display on the display

Data processing systems

Growing community of inventors

Santa Clara, CA, United States of America

Eric Kunze