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Deep neural networks are getting larger. Their implementation on edge and IoTdevices becomes more challenging and moved the community to design lighterversions with similar performance. Standard automatic design tools such as\emph{reinforcement learning} and \emph{evolutionary computing} fundamentallyrely on cheap evaluations of an objective function. In the neural networkdesign context, this objective is the accuracy after training, which isexpensive and time-consuming to evaluate. We automate the design of a lightdeep neural network for image classification using the \emph{Mesh AdaptiveDirect Search}(MADS) algorithm, a mature derivative-free optimization methodthat effectively accounts for the expensive blackbox nature of the objectivefunction to explore the design space, even in the presence of constraints.Ourtests show competitive compression rates with reduced numbers of trials.

Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm