Zobrazeno 1 - 10
of 1 471
pro vyhledávání: '"PAGLIARI, P."'
We study the second-order asymptotic expansion of the $s$-fractional Gagliardo seminorm as $s\to1^-$ in terms of a higher order nonlocal functional. We prove a Mosco-convergence result for the energy functionals and that the $L^2$-gradient flows of t
Externí odkaz:
http://arxiv.org/abs/2410.17829
Autor:
Hamdi, Mohamed Amine, Daghero, Francesco, Sarda, Giuseppe Maria, Van Delm, Josse, Symons, Arne, Benini, Luca, Verhelst, Marian, Pagliari, Daniele Jahier, Burrello, Alessio
Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware accelerators for tensor computations, is becoming one of the crucial
Externí odkaz:
http://arxiv.org/abs/2410.08855
Autor:
Faraone, Gabriele, Daghero, Francesco, Serianni, Eugenio, Licastro, Dario, Di Carolo, Nicola, Grosso, Michelangelo, Franchino, Giovanna Antonella, Pagliari, Daniele Jahier
Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by the top-level integrator for the placement of digital blocks. Specific
Externí odkaz:
http://arxiv.org/abs/2410.07989
Autor:
Risso, Matteo, Goffi, Alessia, Motetti, Beatrice Alessandra, Burrello, Alessio, Bove, Jean Baptiste, Macii, Enrico, Poncino, Massimo, Pagliari, Daniele Jahier, Maffeis, Giuseppe
Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most critical areas that need support. Nonetheless, manually inspecting large a
Externí odkaz:
http://arxiv.org/abs/2410.04802
The demand for executing Deep Neural Networks (DNNs) with low latency and minimal power consumption at the edge has led to the development of advanced heterogeneous Systems-on-Chips (SoCs) that incorporate multiple specialized computing units (CUs),
Externí odkaz:
http://arxiv.org/abs/2409.18566
Autor:
Burrello, Alessio, Carlucci, Francesco, Pollo, Giovanni, Wang, Xiaying, Poncino, Massimo, Macii, Enrico, Benini, Luca, Pagliari, Daniele Jahier
PPG-based Blood Pressure (BP) estimation is a challenging biosignal processing task for low-power devices such as wearables. State-of-the-art Deep Neural Networks (DNNs) trained for this task implement either a PPG-to-BP signal-to-signal reconstructi
Externí odkaz:
http://arxiv.org/abs/2409.07485
Autor:
Pagliari, Roberto, Hill, Peter, Chen, Po-Yu, Dabrowny, Maciej, Tan, Tingsheng, Buet-Golfouse, Francois
In financial applications, regulations or best practices often lead to specific requirements in machine learning relating to four key pillars: fairness, privacy, interpretability and greenhouse gas emissions. These all sit in the broader context of s
Externí odkaz:
http://arxiv.org/abs/2407.12445
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:
Benfenati, Luca, Ingolfsson, Thorir Mar, Cossettini, Andrea, Pagliari, Daniele Jahier, Burrello, Alessio, Benini, Luca
This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process. Initially, it is pre-trained on the extensive Temple University Hospital EEG Corpus (TUEG),
Externí odkaz:
http://arxiv.org/abs/2406.19189
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