Abstract: A control device for tools arranged to apply fasteners in a production process is provided. The control device is configured to, in a production configuration process: obtain a set of trace data for a plurality of test fastening application operations performed by the tools, from said set of trace data, derive maximum and minimum values (Tmax, Tmin) indicative of application force, or a derivative thereof, for each one of a plurality of values indicative of displacement or time (?), and calculate a first and second polynomials based thereon; based on the first and second polynomials, set upper and lower limit curves. In the production process, the control device determines that a fastener application operation is not okay (NOK) if its' trace data goes beyond the upper and lower limit curves. Hereby, a balanced trade-off between the generosity/strictness of the limits is provided to improve accuracy of NOK operation detection.

Abstract: A method for triggering prefetching of memory address translations for memory access requests to be issued by a memory access component of a processor in a data processing system to a memory management function in the data processing system is provided. The method includes obtaining command data from one or more memory access commands in a sequence of memory access commands for the memory access component, predicting one or more memory addresses, for which one or more memory address translations are likely to be required by the memory management function to process one or more memory access requests, from the obtained command data, in response to the predicting, performing one or more trigger operations to trigger a prefetch of the one or more memory address translations, using the predicted one or more memory addresses, in advance of the one or more memory access requests.

Abstract: A method of processing image data of a neural network is performed by a data processing apparatus and comprises writing a first tensor to first storage of the data processing apparatus using a row stride, wherein the first tensor comprises at least one data group, the at least one data group comprising a plurality of data samples and having height, width, and depth dimensions [h, w, c]. The method further comprises transforming the first tensor into a second tensor using a first stride such that the second tensor is a column tensor comprising a plurality of rows, and writing the second tensor to second storage using a second stride that is related to a multiple of the first stride, ?n, such that the second stride covers a first set of memory elements in the second storage into which data samples of a first row of the second tensor are stored and a second set of memory elements into which no data samples from the second tensor are stored.

Abstract: Provided is an access controller configured to control access to a shared resource by plural accessors operable to issue requests for access to the shared resource, the access controller comprising a predictor configured to analyze an activity of an accessor to determine a type of at least one event; predict a future request state of at least one of the accessors based on the determination; and select one of the plurality of accessors to be granted access to the shared resource on a future processor cycle, the selecting being computed using at least the prediction.

Abstract: The present disclosure relates generally to multi-processor arrangements and, more particularly, to a point of serialization for broadcast communications within multi-processor arrangements.

Abstract: The present disclosure relates generally to multi-processor arrangements and, more particularly, to broadcast regions for multi-processor arrangements.

Abstract: A computer implemented method of storing and retrieving feature map data of a neural network the method comprising receiving a first portion of feature map data from local storage, selecting a first set of subportions of the first portion of feature map data, compressing the subportions to produce a first plurality of sections of compressed feature map data and instructing the storage of the sections into external storage. The method also comprises receiving a second plurality of sections of compressed feature map data from the external storage, decompressing the sections to produce a second set of subportions of the second portion of feature map data and storing the second portion of feature map data in local storage. The first and second sets of subportions each correspond to a predetermined format of subdivision and the method comprises selecting the predetermined format of subdivision from a plurality of predetermined formats of subdivision.

Abstract: The present disclosure relates generally to multi-processor arrangements and, more particularly, to broadcast hubs for multi-processor arrangements. A processing tile may comprise a broadcast hub to obtain a plurality of parameters applicable in a particular operation from at least one of a plurality of processing tiles and initiate distribution of the plurality of parameters to the plurality of processing tiles, wherein the plurality of processing tiles may execute the particular operation based at least in part on the plurality of distributed parameters.

Abstract: The present disclosure relates generally to multi-processor arrangements and, more particularly, to broadcast hubs for multi-processor arrangements. A processing tile may comprise a broadcast hub to obtain a plurality of parameters applicable in a particular operation from at least one of a plurality of processing tiles and initiate distribution of the plurality of parameters to the plurality of processing tiles, wherein the plurality of processing tiles may execute the particular operation based at least in part on the plurality of distributed parameters.

Abstract: The present disclosure relates generally to multi-processor arrangements and, more particularly, to broadcast regions for multi-processor arrangements.

Abstract: The present disclosure relates generally to multi-processor arrangements and, more particularly, to a point of serialization for broadcast communications within multi-processor arrangements.

Abstract: A method for adapting a trained neural network is provided. Input data is input to the trained neural network and a plurality of filters are applied to generate a plurality of channels of activation data. Differences between corresponding activation values in the plurality of channels of activation data are calculated and an order of the plurality of channels is determined based on the calculated differences. The neural network is adapted so that it will output channels of activation data in the determined order. The ordering of the channels of activation data is subsequently used to compress activation data values by taking advantage of a correlation between activation data values in adjacent channels.

Abstract: A head-mounted display (HMD) comprising a first side for facing a user of the HMD, a second side opposite to the first side, and a reflective layer for at least partially reflecting incident light incident on the second side. At least one processor of the HMD is configured to obtain luminance data indicative of a luminance of the incident light and control a display device, based on the luminance data, to control a luminance of a portion of emitted light directed towards the user of the HMD during the display of the image. Further examples relate to an HMD with a display device configured to emit light of at least one predetermined wavelength range during display of an image by the display device, and a layer arranged to at least partially prevent transmission of the light of the at least one predetermined wavelength range outward from the HMD.

Abstract: A method for triggering prefetching of memory address translations for memory access requests to be issued by a memory access component of a processor in a data processing system to a memory management function in the data processing system is provided. The method includes obtaining command data from one or more memory access commands in a sequence of memory access commands for the memory access component, predicting one or more memory addresses, for which one or more memory address translations are likely to be required by the memory management function to process one or more memory access requests, from the obtained command data, in response to the predicting, performing one or more trigger operations to trigger a prefetch of the one or more memory address translations, using the predicted one or more memory addresses, in advance of the one or more memory access requests.

Abstract: A method of processing image data of a neural network is performed by a data processing apparatus and comprises writing a first tensor to first storage of the data processing apparatus using a row stride, wherein the first tensor comprises at least one data group, the at least one data group comprising a plurality of data samples and having height, width, and depth dimensions [h, w, c]. The method further comprises transforming the first tensor into a second tensor using a first stride such that the second tensor is a column tensor comprising a plurality of rows, and writing the second tensor to second storage using a second stride that is related to a multiple of the first stride, ?n, such that the second stride covers a first set of memory elements in the second storage into which data samples of a first row of the second tensor are stored and a second set of memory elements into which no data samples from the second tensor are stored.

Abstract: A head-mounted display (HMD) comprising a first side for facing a user of the HMD, a second side opposite to the first side, and a reflective layer for at least partially reflecting incident light incident on the second side. At least one processor of the HMD is configured to obtain luminance data indicative of a luminance of the incident light and control a display device, based on the luminance data, to control a luminance of a portion of emitted light directed towards the user of the HMD during the display of the image. Further examples relate to an HMD with a display device configured to emit light of at least one predetermined wavelength range during display of an image by the display device, and a layer arranged to at least partially prevent transmission of the light of the at least one predetermined wavelength range outward from the HMD.

Abstract: A producer processing unit of a data processing system that is producing a stream of data for use by one or more consumer processing units of the data processing system maintains a record that is accessible to the consumer processing units of a position in the data stream for which it has written data to memory. The consumer processing units then control their reading of the data stream from the memory in accordance with the write position record maintained by the producer processing unit.

Abstract: A method for adapting a trained neural network is provided. Input data is input to the trained neural network and a plurality of filters are applied to generate a plurality of channels of activation data. Differences between corresponding activation values in the plurality of channels of activation data are calculated and an order of the plurality of channels is determined based on the calculated differences. The neural network is adapted so that it will output channels of activation data in the determined order. The ordering of the channels of activation data is subsequently used to compress activation data values by taking advantage of a correlation between activation data values in adjacent channels.

Abstract: A computer implemented method of storing and retrieving feature map data of a neural network the method comprising receiving a first portion of feature map data from local storage, selecting a first set of subportions of the first portion of feature map data, compressing the subportions to produce a first plurality of sections of compressed feature map data and instructing the storage of the sections into external storage. The method also comprises receiving a second plurality of sections of compressed feature map data from the external storage, decompressing the sections to produce a second set of subportions of the second portion of feature map data and storing the second portion of feature map data in local storage. The first and second sets of subportions each correspond to a predetermined format of subdivision and the method comprises selecting the predetermined format of subdivision from a plurality of predetermined formats of subdivision.

Abstract: A method for compressing activation data of a neural network to be written to a storage is provided. The activation data is formed into a plurality of groups and a state indicator indicates whether there are any data elements within each group that have a non-zero value. A second state indicator indicates, for groups having a non-zero value, whether sub-groups within the group contain a data element having a non-zero value. A sub-group state indicator indicates, for each sub-group having a non-zero value, which data elements within that sub-group have a non-zero value. Non-zero values of data elements in the activation data are encoded and a compressed data set is formed comprising the first state indicators, any second state indicators, any sub-group state indicators and the encoded non-zero values.