Abstract: The present disclosure relates to a data processing apparatus for a processing resource to perform a transform operation on an input tensor for the processing resource, said input tensor being formed of a plurality of blocks, each block being a portion of said input tensor capable of being operated on independently of each other, said data processing apparatus comprising: communication circuitry to communicate with a control module and a shared storage of said processing resource; processing circuitry to perform said transform operation, said processing circuitry comprising sub-block processing circuitry and transformation circuitry; and a local storage to store transform operation output from said processing circuitry; wherein said communication circuitry is configured to: receive one or more transform parameters; read a first input sub-block from said shared storage, said first input sub-block being a portion of a first block of said input tensor corresponding to a processing unit of said processing circuit

Abstract: A method of compressing a set of weight values is provided in which an uncompressed set of weight values is obtained, which uncompressed set of weight values includes a plurality of weight values associated with a neural network. A frequently occurring value is identified among the plurality of weight values within the set of weight values and each occurrence of the frequently occurring weight value is replaced within the set of weight values with an index value. The frequently occurring weight value and the index value are associated with the set of weight values. The index value is selected to be less storage intensive than the frequently occurring weight value that it replaces.

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.