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of 278
pro vyhledávání: '"Palesi, Maurizio"'
Autor:
Puigdemont, Pol, Russo, Enrico, Wassington, Axel, Das, Abhijit, Abadal, Sergi, Palesi, Maurizio
Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for efficient computation, leading t
Externí odkaz:
http://arxiv.org/abs/2411.16342
Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Processing Units necessitates classical and quantum-coherent communicati
Externí odkaz:
http://arxiv.org/abs/2406.11452
Multi-core quantum architectures offer a solution to the scalability limitations of traditional monolithic designs. However, dividing the system into multiple chips introduces a critical bottleneck: communication between cores. This paper introduces
Externí odkaz:
http://arxiv.org/abs/2405.16275
Autor:
Blanco, Francesco G., Russo, Enrico, Palesi, Maurizio, Patti, Davide, Ascia, Giuseppe, Catania, Vincenzo
Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery, particularly in meeting stringent execution time constraints, assume
Externí odkaz:
http://arxiv.org/abs/2404.08950
Autor:
Russo, Enrico, Blanco, Francesco Giulio, Palesi, Maurizio, Ascia, Giuseppe, Patti, Davide, Catania, Vincenzo
This paper addresses the critical challenge of managing Quality of Service (QoS) in cloud services, focusing on the nuances of individual tenant expectations and varying Service Level Indicators (SLIs). It introduces a novel approach utilizing Deep R
Externí odkaz:
http://arxiv.org/abs/2403.00766
Autor:
Silvano, Cristina, Ielmini, Daniele, Ferrandi, Fabrizio, Fiorin, Leandro, Curzel, Serena, Benini, Luca, Conti, Francesco, Garofalo, Angelo, Zambelli, Cristian, Calore, Enrico, Schifano, Sebastiano Fabio, Palesi, Maurizio, Ascia, Giuseppe, Patti, Davide, Petra, Nicola, De Caro, Davide, Lavagno, Luciano, Urso, Teodoro, Cardellini, Valeria, Cardarilli, Gian Carlo, Birke, Robert, Perri, Stefania
Recent trends in deep learning (DL) imposed hardware accelerators as the most viable solution for several classes of high-performance computing (HPC) applications such as image classification, computer vision, and speech recognition. This survey summ
Externí odkaz:
http://arxiv.org/abs/2306.15552
Autor:
Alarcón, Eduard, Abadal, Sergi, Sebastiano, Fabio, Babaie, Masoud, Charbon, Edoardo, Bolívar, Peter Haring, Palesi, Maurizio, Blokhina, Elena, Leipold, Dirk, Staszewski, Bogdan, Garcia-Sáez, Artur, Almudever, Carmen G.
The grand challenge of scaling up quantum computers requires a full-stack architectural standpoint. In this position paper, we will present the vision of a new generation of scalable quantum computing architectures featuring distributed quantum cores
Externí odkaz:
http://arxiv.org/abs/2303.14008
The need to efficiently execute different Deep Neural Networks (DNNs) on the same computing platform, coupled with the requirement for easy scalability, makes Multi-Chip Module (MCM)-based accelerators a preferred design choice. Such an accelerator b
Externí odkaz:
http://arxiv.org/abs/2210.14657
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