Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Manuele Rusci"'
Publikováno v:
Sensors, Vol 21, Iss 4, p 1339 (2021)
Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Mor
Externí odkaz:
https://doaj.org/article/d69def0e865747a2afc419d7e29c5e6f
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031236174
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50c8fab582ab55b17a90eb385d94346a
https://doi.org/10.1007/978-3-031-23618-1_41
https://doi.org/10.1007/978-3-031-23618-1_41
Publikováno v:
2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::833875134c01e32ce48484c94dcb0cd6
https://doi.org/10.1109/aicas54282.2022.9869990
https://doi.org/10.1109/aicas54282.2022.9869990
Autor:
Davide Nadalini, Manuele Rusci, Giuseppe Tagliavini, Leonardo Ravaglia, Luca Benini, Francesco Conti
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031150739
An open challenge in making Internet-of-Things sensor nodes "smart'' and self-adaptive is to enable on-chip Deep Neural Network (DNN) training on Ultra-Low-Power (ULP) microcontroller units (MCUs). To this aim, we present a framework, based on PULP-T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c4e934716d5dcc64dbd615e6a8417a2
https://hdl.handle.net/11585/900686
https://hdl.handle.net/11585/900686
Publikováno v:
AICAS
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Given the recent advances in the design of efficient Deep Neural Networks (DNN) for tiny edge devices, the feature extraction frontend has become a computation bottleneck for enabling audio processing on low-end MicroController Units (MCUs). To addre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8189723457be13cf89f8aeb373fef211
https://doi.org/10.1109/aicas51828.2021.9458491
https://doi.org/10.1109/aicas51828.2021.9458491
Publikováno v:
Sensors
Volume 21
Issue 4
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 1339, p 1339 (2021)
Sensors, 21 (4)
Volume 21
Issue 4
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 1339, p 1339 (2021)
Sensors, 21 (4)
Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Mor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6fd2f15414069d2a2b84fb8850701d8
Autor:
Leonardo Ravaglia, Francesco Conti, Manuele Rusci, Alessandro Capotondi, Davide Nadalini, Luca Benini
In the last few years, research and development on Deep Learning models and techniques for ultra-low-power devices in a word, TinyML has mainly focused on a train-then-deploy assumption, with static models that cannot be adapted to newly collected da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f551185826fd11c021615df0ee10d8e
Autor:
Andrea Samore, Serena Morigi, Damiana Lazzaro, Luca Benini, Manuele Rusci, Patrizia Melpignano
An automatic tool, targeting low-cost, low-power, point-of-care embedded system, is proposed for fluorescence diagnostic imaging. This allows for a quick and accurate diagnosis even when used by nonexpert operators. To achieve this goal, an embedded
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b9c20fcad33ba3512f6d64234656a6a
http://hdl.handle.net/11585/771945
http://hdl.handle.net/11585/771945
Publikováno v:
The Frontiers Collection ISBN: 9783030183370
One of the key goals for the next decade is to push machine learning into sensors at the edge, for always-on operation within a sub-mW power budget. However, to achieve this goal, we need to address memory organization challenges, as current machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::382eb9d89580ce42bb085e28291ad4f3
https://doi.org/10.1007/978-3-030-18338-7_19
https://doi.org/10.1007/978-3-030-18338-7_19
Autor:
Vincenzo Lomonaco, Alessandro Capotondi, Lorenzo Pellegrini, Manuele Rusci, Davide Maltoni, Luca Benini, Leonardo Ravaglia, Francesco Conti
Publikováno v:
SiPS
AI-powered edge devices currently lack the ability to adapt their embedded inference models to the ever-changing environment. To tackle this issue, Continual Learning (CL) strategies aim at incrementally improving the decision capabilities based on n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fca09f8839abb2f8f226191ef141522