  Running Convolutional Neural Network (CNN) based applications on edge devices
near the source of data can meet the latency and privacy challenges. However
due to their reduced computing resources and their energy constraints, these
edge devices can hardly satisfy CNN needs in processing and data storage. For
these platforms, choosing the CNN with the best trade-off between accuracy and
execution time while respecting Hardware constraints is crucial. In this paper,
we present and compare five (5) of the widely used Machine Learning based
methods for execution time prediction of CNNs on two (2) edge GPU platforms.
For these 5 methods, we also explore the time needed for their training and
tuning their corresponding hyperparameters. Finally, we compare times to run
the prediction models on different platforms. The utilization of these methods
will highly facilitate design space exploration by providing quickly the best
CNN on a target edge GPU. Experimental results show that eXtreme Gradient
Boosting (XGBoost) provides a less than 14.73% average prediction error even
for unexplored and unseen CNN models' architectures. Random Forest (RF) depicts
comparable accuracy but needs more effort and time to be trained. The other 3
approaches (OLS, MLP and SVR) are less accurate for CNN performances
estimation.
