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pro vyhledávání: '"Sarda, Giuseppe Maria"'
Autor:
Van Delm, Josse, Vandersteegen, Maarten, Burrello, Alessio, Sarda, Giuseppe Maria, Conti, Francesco, Pagliari, Daniele Jahier, Benini, Luca, Verhelst, Marian
Publikováno v:
2023 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2023, pp. 1-6
Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they typically contain multiple het
Externí odkaz:
http://arxiv.org/abs/2406.07453
Autor:
Risso, Matteo, Burrello, Alessio, Sarda, Giuseppe Maria, Benini, Luca, Macii, Enrico, Poncino, Massimo, Verhelst, Marian, Pagliari, Daniele Jahier
The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a diverse set of hardware accelerators. How to optimally map a DNN onto su
Externí odkaz:
http://arxiv.org/abs/2306.05060
Autor:
Hanif, Muhammad Abdullah, Sarda, Giuseppe Maria, Marchisio, Alberto, Masera, Guido, Martina, Maurizio, Shafique, Muhammad
In today's era of smart cyber-physical systems, Deep Neural Networks (DNNs) have become ubiquitous due to their state-of-the-art performance in complex real-world applications. The high computational complexity of these networks, which translates to
Externí odkaz:
http://arxiv.org/abs/2208.00331