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In response to the increasing interest in Internet of Things (IoT) applications, several studies explore ways to reduce the size of Deep Neural Networks (DNNs), to allow implementations on edge devices with strongly constrained resources. To this aim, pruning allows removing redundant interconnections between neurons, thus reducing a DNN memory footprint and computational complexity, while also minimizing the performance loss. Over the last years, many works presenting new pruning techniques and prunable architectures have been proposed but relatively little effort has been devoted to implementing and validating their performance on hardware. Recently, we introduced neurons based on the Multiply-And-Maximin (MAM) map-reduce paradigm. When state-of-the-art unstructured pruning techniques are applied, MAM-based neurons have shown better pruning capabilities compared to standard neurons …