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.