  In this work, we leverage ensemble learning as a tool for the creation of
faster, smaller, and more accurate deep learning models. We demonstrate that we
can jointly optimize for accuracy, inference time, and the number of parameters
by combining DNN classifiers. To achieve this, we combine multiple ensemble
strategies: bagging, boosting, and an ordered chain of classifiers. To reduce
the number of DNN ensemble evaluations during the search, we propose EARN, an
evolutionary approach that optimizes the ensemble according to three objectives
regarding the constraints specified by the user. We run EARN on 10 image
classification datasets with an initial pool of 32 state-of-the-art DCNN on
both CPU and GPU platforms, and we generate models with speedups up to
$7.60\times$, reductions of parameters by $10\times$, or increases in accuracy
up to $6.01\%$ regarding the best DNN in the pool. In addition, our method
generates models that are $5.6\times$ faster than the state-of-the-art methods
for automatic model generation.
