Abstract: An input data array is subjected to neural network processing to generate a result of the neural network processing for the input data array. A perturbation is applied to a part (but not all of) the input data array, with neural network processing then performed using the so-perturbed version of the input data array. However only some (and not all) of the perturbed version is subjected to neural network processing, based on the part of the input data array to which the perturbation has been applied. The result of the neural network processing of the perturbed version of the input data array is compared with the result of the neural network processing of the input data array without the perturbation, to determine whether the perturbation of the input data array has an effect on the result of the neural network processing.

Abstract: When executing a neural network comprising a sequence of plural layers of neural network processing in which at least one of the layers of the sequence of plural layers of the neural network is followed by two or more branches of neural network processing, each branch comprising a different sequence of one or more layers of neural network processing, the branch or branches to use for the neural network processing following the layer of the neural network that is followed by the two or more branches of neural network processing is selected based on a property or properties of the output feature map from the layer that is followed by the two or more branches.

Abstract: A computer implemented method, performed in a data processing system comprising a performance monitoring unit. The method comprises receiving a set of computer-readable instructions to be executed by the data processing system to implement at least a portion of a neural network, wherein one or more of the instructions is labeled with one or more performance monitoring labels based upon one or more features of the neural network. The method further comprises configuring the performance monitoring unit to count one or more events occurring in one or more components of the data processing system based on the one or more performance monitoring labels.

Abstract: There is provided a data processing apparatus for performing machine learning. The data processing apparatus includes convolution circuitry for convolving a plurality of neighbouring regions of input data using a kernel to produce convolution outputs. Max-pooling circuitry determines and selects the largest of the convolution outputs as a pooled output and prediction circuitry performs a size prediction of the convolution outputs based on the neighbouring regions, wherein the size prediction is performed prior to the max-pooling circuitry determining the largest of the convolution outputs and adjusts a behaviour of the convolution circuitry based on the size prediction.

Abstract: A system-on-chip comprises processing circuitry to process input data to generate output data, and power management circuitry to control power management policy for at least a portion of the system-on-chip. The power management circuitry controls the power management policy depending on metadata indicative of a property of the input data to be processed by the processing circuitry.

Abstract: A method for optimizing machine learning processing is provided. The method comprising retrieving, neural network architecture information for a neural network, the neural network architecture information comprising layer information and kernel information for the neural network. The network architecture information is analyzed to identify convolutional layers in the neural network which have associated strided layers. A first kernel for a convolutional layer identified as having an associated strided layer, and a second kernel for the strided layer associated with the convolutional layer are retrieved. A composite kernel is then generated, based on the first and second kernel, that performs the functions of the first and second kernel. Finally, the composite kernel is stored for further use by a neural network.

Abstract: A processing unit is provided which comprises volatile storage for storing machine learning data in binary representation, and a data processing engine communicatively coupled to the volatile storage. The processing unit is configured to selectively invert the bit values in binary representations of portions of the machine learning data when performing storage operations using the volatile storage. A computer-implemented method, and non-transitory computer-readable storage medium comprising instructions for executing the method are also provided. The method comprises receiving a request to perform a storage operation on the volatile storage using the machine learning data and performing the storage operation, including, selecting a portion of the machine learning data and inverting bit values in a binary representation of the selected portion. A computer-implemented method comprising receiving a request to store machine learning data on volatile storage and storing the machine learning data is also provided.

Abstract: A computer implemented method, performed in a data processing system comprising a performance monitoring unit. The method comprises receiving a set of computer-readable instructions to be executed by the data processing system to implement at least a portion of a neural network, wherein one or more of the instructions is labeled with one or more performance monitoring labels based upon one or more features of the neural network. The method further comprises configuring the performance monitoring unit to count one or more events occurring in one or more components of the data processing system based on the one or more performance monitoring labels.