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The growing interest in Internet of Things (IoT) and mobile biomedical applications is pushing the investigation on approaches that can be used to reduce the energy consumption while acquiring data. Compressed Sensing (CS) is a technique that allows to reduce the energy required for the acquisition and compression of a sparse signal, transferring the complexity to the reconstruction stage. Many works leverage the use of Deep Neural Networks (DNNs) for signal reconstruction and, assuming that also this operation has to be performed on a IoT device, it is necessary for the DNN architecture to fit in small and low-energy devices. Pruning techniques, that can reduce the size of DNNs by removing unnecessary parameters and thus decreasing storage requirements, can be of great help in this effort. In this work, a novel Multiply and Max&Min (MAM²) map-reduce paradigm trained with the vanishing contributes …