Search Results for author: Michele Magno

Found 6 papers, 2 papers with code

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

no code implementations12 Jan 2021 Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli, Michele Magno, Elisabetta Farella, Luca Benini

This BNN reaches a 77. 9% accuracy, just 7% lower than the full-precision version, with 58 kB (7. 2 times less) for the weights and 262 kB (2. 4 times less) memory in total.

Event Detection Object Recognition +2

TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars

1 code implementation25 Jun 2020 Moritz Scherer, Michele Magno, Jonas Erb, Philipp Mayer, Manuel Eggimann, Luca Benini

Furthermore, the gesture recognition classifier has been implemented on a Parallel Ultra-Low Power Processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 100 mW is achieved.

Hand Gesture Recognition Hand-Gesture Recognition

An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing

no code implementations31 Mar 2020 Xiaying Wang, Michael Hersche, Batuhan Tömekce, Burak Kaya, Michele Magno, Luca Benini

Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0. 31% with 7. 6x memory footprint reduction and a small accuracy loss of 2. 51% with 15x reduction.

Edge-computing EEG +1

HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data

no code implementations10 Dec 2019 Xiaying Wang, Lukas Cavigelli, Manuel Eggimann, Michele Magno, Luca Benini

Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0. 5m/px.

Scene Segmentation

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

1 code implementation8 Nov 2019 Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini

The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network.

Edge-computing

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

no code implementations9 Nov 2016 Lukas Cavigelli, Dominic Bernath, Michele Magno, Luca Benini

The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats.

General Classification Scene Labeling

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