Firstly, it should once again be noted that execution times are not universal and might differ drastically between machines executing the timing script. Thus, the depicted execution times (Figure ) must be taken as relative measures. Next to the times for the conventional method, execution of the shortcut model on CPU and GPU are depicted. When using GPU acceleration, one may assign varying batch sizes for the evaluation batch passed into the network for prediction. Here, we show speedups for a batch size of ℬ = 100 and ℬ = 1000 since further increasing ℬ does not yield any significant speedup (for full results see SI). It is apparent that ffNN execution on CPU is by far the fastest of the depicted methods. Interestingly GPU execution does not seem to achieve any speed-up, even for higher numbers of samples. However, speed-up between conventional methods and ffNN is in the range of one to two orders of magnituderegardless whether executed on CPU or GPUmaking the method accessible for users without access to a dedicated GPU on their machine. The speedup factors found in this work are in good agreement with typical values of around 70 reported by