A method of implementing a traditional computer vision algorithm on a neural network hardware accelerator that supports a limited number of neural network primitives, the traditional computer vision algorithm being a computer vision algorithm that is not in the form of a trainable neural network, the method comprising: receiving, by a system comprising the neural network hardware accelerator and a converter, a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations which form the traditional computer vision algorithm ; mapping, by the converter, each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation , with each of the one or more neural network primitives of each set being a neural network primitive that is supported by the neural network hardware accelerator; linking, by the converter, the one or more neural network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm ; and processing, using the neural network accelerator, input data in accordance with the neural network that represents the traditional computer vision algorithm to generate output data for the neural network ; wherein the limited number of neural network primitives that is supported by the neural network hardware accelerator comprises at least two different neural network primitives from a set of neural network primitives that comprises a convolution primitive, a fully-connected primitive, a normalisation primitive, a pooling primitive, an activation primitive and an element-wise operation primitive. receiving, by a system comprising the neural network hardware accelerator and a converter, a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations which form the traditional computer vision algorithm ; mapping, by the converter, each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation , with each of the one or more neural network primitives of each set being a neural network primitive that is supported by the neural network hardware accelerator; linking, by the converter, the one or more neural network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm ; and processing, using the neural network accelerator, input data in accordance with the neural network that represents the traditional computer vision algorithm to generate output data for the neural network ; wherein the limited number of neural network primitives that is supported by the neural network hardware accelerator comprises at least two different neural network primitives from a set of neural network primitives that comprises a convolution primitive, a fully-connected primitive, a normalisation primitive, a pooling primitive, an activation primitive and an element-wise operation primitive. <EOS>