Aggressively prunable MAM²-based Deep Neural Oracle for ECG acquisition by Compressed Sensing

Philippe Bich, Luciano Prono, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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 technique and then pruned with the activation rate method is proposed. The result is a naturally and aggressively pruned DNN layer structure. This structure is used to reduce the complexity of a DNN-based CS reconstructor and its performance is verified. As an example, MAM²-based layers still retain the baseline accuracy of the CS decoder with 94% of the parameters pruned against 25% when using classic MAC-based layers only.
Original languageEnglish (US)
Title of host publicationBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781665469173
StatePublished - Jan 1 2022
Externally publishedYes


Dive into the research topics of 'Aggressively prunable MAM²-based Deep Neural Oracle for ECG acquisition by Compressed Sensing'. Together they form a unique fingerprint.

Cite this