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of 109
pro vyhledávání: '"Pau, Danilo"'
Autor:
Ayaz, Ferheen, Zakariyya, Idris, Cano, José, Keoh, Sye Loong, Singer, Jeremy, Pau, Danilo, Kharbouche-Harrari, Mounia
Reducing the memory footprint of Machine Learning (ML) models, particularly Deep Neural Networks (DNNs), is essential to enable their deployment into resource-constrained tiny devices. However, a disadvantage of DNN models is their vulnerability to a
Externí odkaz:
http://arxiv.org/abs/2304.12829
Autor:
Sahoo, Piyush, Rajoria, Romesh, Chandhok, Shivam, Darak, S. J., Pau, Danilo, Dabral, Hem-Dutt
With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains. Narrow beam-forming makes it difficult to have
Externí odkaz:
http://arxiv.org/abs/2107.11070
Autor:
Banbury, Colby, Reddi, Vijay Janapa, Torelli, Peter, Holleman, Jeremy, Jeffries, Nat, Kiraly, Csaba, Montino, Pietro, Kanter, David, Ahmed, Sebastian, Pau, Danilo, Thakker, Urmish, Torrini, Antonio, Warden, Peter, Cordaro, Jay, Di Guglielmo, Giuseppe, Duarte, Javier, Gibellini, Stephen, Parekh, Videet, Tran, Honson, Tran, Nhan, Wenxu, Niu, Xuesong, Xu
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted and easily reproducible benchmark for these
Externí odkaz:
http://arxiv.org/abs/2106.07597
Autor:
Crocioni, Giulia, Gruosso, Giambattista, Pau, Danilo, Denaro, Davide, Zambrano, Luigi, di Giore, Giuseppe
Nowadays, Neural Networks represent a major expectation for the realization of powerful Deep Learning algorithms, which can determine several physical systems' behaviors and operations. Computational resources required for model, training, and runnin
Externí odkaz:
http://arxiv.org/abs/2103.00201
Autor:
Pau, Danilo Pietro1 (AUTHOR) danilo.pau@st.com, Aymone, Fabrizio Maria1 (AUTHOR)
Publikováno v:
Eng. Mar2024, Vol. 5 Issue 1, p34-50. 17p.
Autor:
Banbury, Colby R., Reddi, Vijay Janapa, Lam, Max, Fu, William, Fazel, Amin, Holleman, Jeremy, Huang, Xinyuan, Hurtado, Robert, Kanter, David, Lokhmotov, Anton, Patterson, David, Pau, Danilo, Seo, Jae-sun, Sieracki, Jeff, Thakker, Urmish, Verhelst, Marian, Yadav, Poonam
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted benchmark for these systems. Benchmarkin
Externí odkaz:
http://arxiv.org/abs/2003.04821
Autor:
Pau, Danilo1 (AUTHOR) aptrp99@gmail.com, Pisani, Andrea1 (AUTHOR), Candelieri, Antonio2 (AUTHOR) antonio.candelieri@unimib.it
Publikováno v:
Algorithms. Jan2024, Vol. 17 Issue 1, p22. 18p.
Akademický článek
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Echo State Networks (ESN) are a class of Recurrent Neural Networks (RNN) that has gained substantial popularity due to their effectiveness, ease of use and potential for compact hardware implementation. An ESN contains the three network layers input,
Externí odkaz:
http://arxiv.org/abs/1807.09510
Publikováno v:
In Expert Systems With Applications 30 November 2022 207