  Control of the surface texture of steel strip during the galvanizing and
temper rolling processes is essential to satisfy customer requirements and is
conventionally measured post-production using a stylus. In-production laser
reflection measurement is less consistent than physical measurement but enables
real time adjustment of processing parameters to optimize product surface
characteristics. We propose the use of machine learning to improve accuracy of
the transformation from inline laser reflection measurements to a prediction of
surface properties. In addition to accuracy, model evaluation speed is
important for fast feedback control. The ROCKET model is one of the fastest
state of the art models, however it can be sped up by utilizing a GPU. Our
contribution is to implement the model in PyTorch for fast GPU kernel
transforms and provide a soft version of the Proportion of Positive Values
(PPV) nonlinear pooling function, allowing gradient flow. We perform timing and
performance experiments comparing the implementations
