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The choice of convolutional routines (primitives) to implement neural networks has a tremendous impact on their inference performance (execution speed) on a given hardware platform. To optimise a neural network by primitive selection, the optimal primitive is identified for each layer of the network. This process requires a lengthy profiling stage, iterating over all the available primitives for each layer configuration, to measure their execution time on the target platform. Because each primitive exploits the hardware in different ways, new profiling is needed to obtain the best performance when moving to another platform. In this work, we propose to replace this prohibitively expensive profiling stage with a machine learning based approach of performance modeling. Our approach speeds up the optimisation time drastically. After training, our performance model can estimate the performance of convolutional primitives in any layer configuration. The time to optimise the execution of large neural networks via primitive selection is reduced from hours to just seconds. Our performance model is easily transferable to other target platforms. We demonstrate this by training a performance model on an Intel platform and performing transfer learning to AMD and ARM processor devices with minimal profiled samples.