  The computational workload involved in Convolutional Neural Networks (CNNs)
is typically out of reach for low-power embedded devices. There are a large
number of approximation techniques to address this problem. These methods have
hyper-parameters that need to be optimized for each CNNs using design space
exploration (DSE). The goal of this work is to demonstrate that the DSE phase
time can easily explode for state of the art CNN. We thus propose the use of an
optimized exploration process to drastically reduce the exploration time
without sacrificing the quality of the output.
