activity
20202022
most citedCompact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms

58 citations · 103 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD2022

Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks

Gianmarco Cerutti, Lukas Cavigelli, Renzo Andri +3

Keyword spotting (KWS) is a crucial function enabling the interaction with the many ubiquitous smart devices in our surroundings, either activating them through wake-word or direct…

cs.CV20211 cited

PhiNets: a scalable backbone for low-power AI at the edge

Francesco Paissan, Alberto Ancilotto, Elisabetta Farella

In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has becom…

cs.CV20215 cited

Enabling energy efficient machine learning on a Ultra-Low-Power vision sensor for IoT

Francesco Paissan, Massimo Gottardi, Elisabetta Farella

The Internet of Things (IoT) and smart city paradigm includes ubiquitous technology to extract context information in order to return useful services to users and citizens. An esse…

cs.LG202139 cited

Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices

Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli +3

Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on Deep Neural Networks are very effective, but highly demanding in te…

eess.AS202058 cited

Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms

Gianmarco Cerutti, Rahul Prasad, Alessio Brutti +1

Outdoor acoustic events detection is an exciting research field but challenged by the need for complex algorithms and deep learning techniques, typically requiring many computation…