Before revision:  Using models with less than 75 parameters, and only 250 training data instances, we are able to obtain a low MAPE of ~13\% significantly outperforming traditional feed-forward neural networks on 40 kernel-variant-hardware combinations.
After revision:  Using models with less than 75 parameters, and only 250 training data instances, we are able to obtain a low MAPE of 3\%, significantly outperforming traditional feed-forward neural networks on 40 kernel-variant-hardware combinations.