no code implementations • 23 Feb 2024 • Matteo Risso, Francesco Daghero, Beatrice Alessandra Motetti, Daniele Jahier Pagliari, Enrico Macii, Massimo Poncino, Alessio Burrello
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring.
no code implementations • 2 Feb 2024 • Matteo Risso, Chen Xie, Francesco Daghero, Alessio Burrello, Seyedmorteza Mollaei, Marco Castellano, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Low-resolution infrared (IR) array sensors enable people counting applications such as monitoring the occupancy of spaces and people flows while preserving privacy and minimizing energy consumption.
no code implementations • 10 Jan 2024 • Khaled Sidahmed Sidahmed Alamin, Francesco Daghero, Giovanni Pollo, Daniele Jahier Pagliari, Yukai Chen, Enrico Macii, Massimo Poncino, Sara Vinco
Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems.
1 code implementation • 16 Jun 2023 • Francesco Daghero, Alessio Burrello, Enrico Macii, Paolo Montuschi, Massimo Poncino, Daniele Jahier Pagliari
With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes.
no code implementations • 12 Apr 2023 • Chen Xie, Francesco Daghero, Yukai Chen, Marco Castellano, Luca Gandolfi, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Ultra-low-resolution Infrared (IR) array sensors offer a low-cost, energy-efficient, and privacy-preserving solution for people counting, with applications such as occupancy monitoring.
no code implementations • 2 Sep 2022 • Francesco Daghero, Alessio Burrello, Chen Xie, Marco Castellano, Luca Gandolfi, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
With experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we show that (i) We are able to obtain a rich set of Pareto-optimal CNNs for HAR, spanning more than 1 order of magnitude in terms of memory, latency and energy consumption; (ii) Thanks to adaptive inference, we can derive >20 runtime operating modes starting from a single CNN, differing by up to 10% in classification scores and by more than 3x in inference complexity, with a limited memory overhead; (iii) on three of the four benchmarks, we outperform all previous deep learning methods, reducing the memory occupation by more than 100x.
no code implementations • 7 Jun 2022 • Francesco Daghero, Daniele Jahier Pagliari, Massimo Poncino
In this work, we bridge the gap between on-device HAR and DL thanks to a hierarchical architecture composed of a decision tree (DT) and a one dimensional Convolutional Neural Network (1D CNN).
no code implementations • 27 May 2022 • Francesco Daghero, Alessio Burrello, Chen Xie, Luca Benini, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
The accuracy of a RF often increases with the number of internal weak learners (decision trees), but at the cost of a proportional increase in inference latency and energy consumption.
1 code implementation • 25 May 2022 • Francesco Daghero, Chen Xie, Daniele Jahier Pagliari, Alessio Burrello, Marco Castellano, Luca Gandolfi, Andrea Calimera, Enrico Macii, Massimo Poncino
In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set.
no code implementations • 22 Apr 2022 • Chen Xie, Francesco Daghero, Yukai Chen, Marco Castellano, Luca Gandolfi, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN).
no code implementations • 7 Apr 2022 • Francesco Daghero, Alessio Burrello, Daniele Jahier Pagliari, Luca Benini, Enrico Macii, Massimo Poncino
Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy.