METHODS AND APPARATUSES FOR INFERENCING USING A NEURAL NETWORK

This specification describes methods for performing inferencing based on input data, the methods comprising: initialising a neural network based on a set of stored model information, which defines a plurality of orthogonal binary basis vectors which are to be used to implement kernels in one or more hidden layers of the neural network, and plural sets of plural coefficients, each set of plural coefficients corresponding to a respective one of the kernels, wherein each of the coefficients in a given set of coefficients is associated with a respective one of the one or more orthogonal binary basis vectors; passing input data through the neural network such that convolution operations between the kernels and data arriving at the kernels are performed, wherein each of the kernels is implemented using a respective set of coefficients and the orthogonal binary basis vectors with which the coefficients in the set are associated; and outputting data from the neural network, the output data representing an inference corresponding to the input data. The specification also describes methods for generating model information based on which neural networks may be initialised.