  Automatic search of neural network architectures is a standing research
topic. In addition to the fact that it presents a faster alternative to
hand-designed architectures, it can improve their efficiency and for instance
generate Convolutional Neural Networks (CNN) adapted for mobile devices. In
this paper, we present a multi-objective neural architecture search method to
find a family of CNN models with the best accuracy and computational resources
tradeoffs, in a search space inspired by the state-of-the-art findings in
neural search. Our work, called Dvolver, evolves a population of architectures
and iteratively improves an approximation of the optimal Pareto front. Applying
Dvolver on the model accuracy and on the number of floating points operations
as objective functions, we are able to find, in only 2.5 days, a set of
competitive mobile models on ImageNet. Amongst these models one architecture
has the same Top-1 accuracy on ImageNet as NASNet-A mobile with 8% less
floating point operations and another one has a Top-1 accuracy of 75.28% on
ImageNet exceeding by 0.28% the best MobileNetV2 model for the same
computational resources.
