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The computational workload involved in Convolutional Neural Networks (CNNs)is typically out of reach for low-power embedded devices. There are a largenumber of approximation techniques to address this problem. These methods havehyper-parameters that need to be optimized for each CNNs using design spaceexploration (DSE). The goal of this work is to demonstrate that the DSE phasetime can easily explode for state of the art CNN. We thus propose the use of anoptimized exploration process to drastically reduce the exploration timewithout sacrificing the quality of the output.

Fast Exploration of Weight Sharing Opportunities for CNN Compression