Zobrazeno 1 - 10
of 812
pro vyhledávání: '"Macii, Enrico"'
Neuromorphic models take inspiration from the human brain by adopting bio-plausible neuron models to build alternatives to traditional Machine Learning (ML) and Deep Learning (DL) solutions. The scarce availability of dedicated hardware able to actua
Externí odkaz:
http://arxiv.org/abs/2407.04076
Autor:
Motetti, Beatrice Alessandra, Risso, Matteo, Burrello, Alessio, Macii, Enrico, Poncino, Massimo, Pagliari, Daniele Jahier
The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mixed-precision quantization, which lead to latency and memory occupatio
Externí odkaz:
http://arxiv.org/abs/2407.01054
Autor:
Daghero, Francesco, Burrello, Alessio, Poncino, Massimo, Macii, Enrico, Pagliari, Daniele Jahier
Depthwise separable convolutions are a fundamental component in efficient Deep Neural Networks, as they reduce the number of parameters and operations compared to traditional convolutions while maintaining comparable accuracy. However, their low data
Externí odkaz:
http://arxiv.org/abs/2406.12478
Autor:
Benfenati, Luca, Pagliari, Daniele Jahier, Zanatta, Luca, Velez, Yhorman Alexander Bedoya, Acquaviva, Andrea, Poncino, Massimo, Macii, Enrico, Benini, Luca, Burrello, Alessio
Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use
Externí odkaz:
http://arxiv.org/abs/2404.02944
Autor:
Hamdi, Mohamed Amine, Pollo, Giovanni, Risso, Matteo, Haugou, Germain, Burrello, Alessio, Macii, Enrico, Poncino, Massimo, Vinco, Sara, Pagliari, Daniele Jahier
RISC-V cores have gained a lot of popularity over the last few years. However, being quite a recent and novel technology, there is still a gap in the availability of comprehensive simulation frameworks for RISC-V that cover both the functional and ex
Externí odkaz:
http://arxiv.org/abs/2404.01861
Autor:
Risso, Matteo, Daghero, Francesco, Motetti, Beatrice Alessandra, Pagliari, Daniele Jahier, Macii, Enrico, Poncino, Massimo, Burrello, Alessio
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonetheless, their size and simple electronics pose severe challenges in implem
Externí odkaz:
http://arxiv.org/abs/2402.15273
Autor:
Risso, Matteo, Xie, Chen, Daghero, Francesco, Burrello, Alessio, Mollaei, Seyedmorteza, Castellano, Marco, Macii, Enrico, Poncino, Massimo, Pagliari, Daniele Jahier
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. Deep Neural Networks (DNNs) have been shown to be w
Externí odkaz:
http://arxiv.org/abs/2402.01226
Autor:
Alamin, Khaled Sidahmed Sidahmed, Daghero, Francesco, Pollo, Giovanni, Pagliari, Daniele Jahier, Chen, Yukai, Macii, Enrico, Poncino, Massimo, Vinco, Sara
Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it at run time, a model is required for its estimation. Recently, several
Externí odkaz:
http://arxiv.org/abs/2401.05474
Autor:
Burrello, Alessio, Risso, Matteo, Motetti, Beatrice Alessandra, Macii, Enrico, Benini, Luca, Pagliari, Daniele Jahier
The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can run on low-power devices. However, traditional DL models tend to be too
Externí odkaz:
http://arxiv.org/abs/2310.07217
Autor:
Capogrosso, Luigi, Mascolini, Alessio, Girella, Federico, Skenderi, Geri, Gaiardelli, Sebastiano, Dall'Ora, Nicola, Ponzio, Francesco, Fraccaroli, Enrico, Di Cataldo, Santa, Vinco, Sara, Macii, Enrico, Fummi, Franco, Cristani, Marco
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, In
Externí odkaz:
http://arxiv.org/abs/2307.06975