Abstract: A mechanism for processing, on a hardware accelerator comprising fixed-function circuitry, data according to a neural network process that comprises a neural network with an associated argmax or argmin function. The argmax or argmin function is mapped to a set of elementary neural network operations available to the fixed-function circuitry. The neural network process is then executed using the fixed-function circuitry. The data processed using the neural network process comprises image and/or audio data.

Abstract: A computer-implemented method of selecting a number format for representing two or more values of a recurrent neural network (RNN) for use in configuring a hardware implementation of the RNN, includes receiving a representation of the RNN; implementing the representation of the RNN as a test neural network for operation on a sequence of test inputs, each step of the test neural network comprising an instance of the two or more values of the RNN; operating the test neural network for a plurality of steps on the sequence of test inputs and collecting statistics for provision to a number format selection algorithm; and applying a number format selection algorithm to the statistics so as to derive a common number format for the plurality of instances of the two or more values of the RNN.

Abstract: A computer implemented method of training a neural network configured to combine a set of coefficients with respective input data values. So as to train a test implementation of the neural network, sparsity is applied to one or more of the coefficients according to a sparsity parameter, the sparsity parameter indicating a level of sparsity to be applied to the set of coefficients; the test implementation of the neural network is operated on training input data using the coefficients so as to form training output data; in dependence on the training output data, assessing the accuracy of the neural network; the sparsity parameter is updated in dependence on the accuracy of the neural network; and a runtime implementation of the neural network is configured in dependence on the updated sparsity parameter.

Abstract: A computer implemented method of training a neural network configured to combine a set of coefficients with respective input data values. So as to train a test implementation of the neural network, sparsity is applied to one or more of the coefficients according to a sparsity parameter, the sparsity parameter indicating a level of sparsity to be applied to the set of coefficients; the test implementation of the neural network is operated on training input data using the coefficients so as to form training output data; in dependence on the training output data, assessing the accuracy of the neural network; the sparsity parameter is updated in dependence on the accuracy of the neural network; and a runtime implementation of the neural network is configured in dependence on the updated sparsity parameter.

Abstract: A mechanism for processing, on a hardware accelerator comprising fixed-function circuitry, data according to a neural network process that includes a pooling, unpooling or backward pooling and/or binary argmax/argmin function. The function is mapped to a set of elementary neural network operations available to the fixed-function circuitry. The neural network process is then executed using the fixed-function circuitry. The data processed using the neural network process comprises image and/or audio data.

Abstract: A mechanism for processing, on a hardware accelerator comprising fixed-function circuitry, data according to a neural network process that comprises a neural network with an associated argmax or argmin function. The argmax or argmin function is mapped to a set of elementary neural network operations available to the fixed-function circuitry. The neural network process is then executed using the fixed-function circuitry. The data processed using the neural network process comprises image and/or audio data.

Abstract: A computer implemented method of training a neural network configured to combine a set of coefficients with respective input data values. So as to train a test implementation of the neural network, sparsity is applied to one or more of the coefficients according to a sparsity parameter, the sparsity parameter indicating a level of sparsity to be applied to the set of coefficients; the test implementation of the neural network is operated on training input data using the coefficients so as to form training output data; in dependence on the training output data, assessing the accuracy of the neural network; the sparsity parameter is updated in dependence on the accuracy of the neural network; and a runtime implementation of the neural network is configured in dependence on the updated sparsity parameter.

Abstract: A method of compressing a set of coefficients for subsequent use in a neural network, the method comprising: applying sparsity to a plurality of groups of the coefficients, each group comprising a predefined plurality of coefficients; and compressing the groups of coefficients according to a compression scheme aligned with the groups of coefficients so as to represent each group of coefficients by an integer number of one or more compressed values.

Abstract: A method of implementing in hardware a recurrent neural network (RNN) for operation on a sequence of inputs, each step of the recurrent neural network being for operation on a different input of the sequence, the method comprising: receiving a representation of the RNN; transforming the representation of the RNN into a derivative neural network for operation over a predetermined plurality of inputs of the sequence of inputs, the derivative neural network having one or more state inputs and one or more state outputs and being equivalent to the RNN over a predetermined plurality of steps of the RNN; and iteratively applying the derivative neural network to the sequence of inputs by: implementing a sequence of instances of the derivative neural network in hardware; and providing the one or more state outputs from each instance of the derivative neural network at the hardware as the one or more state inputs to a subsequent instance of the derivative neural network at the hardware so as to operate the RNN over a s

Abstract: A computer-implemented method of selecting a number format for representing two or more values of a recurrent neural network (RNN) for use in configuring a hardware implementation of the RNN, includes receiving a representation of the RNN; implementing the representation of the RNN as a test neural network for operation on a sequence of test inputs, each step of the test neural network comprising an instance of the two or more values of the RNN; operating the test neural network for a plurality of steps on the sequence of test inputs and collecting statistics for provision to a number format selection algorithm; and applying a number format selection algorithm to the statistics so as to derive a common number format for the plurality of instances of the two or more values of the RNN.