Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Guirado, Robert"'
Autor:
Guirado, Robert, Rahimi, Abbas, Karunaratne, Geethan, Alarcón, Eduard, Sebastian, Abu, Abadal, Sergi
Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using long random vectors known as hypervectors. Among different hardware platforms capable of executing HDC algorithms, in-memory
Externí odkaz:
http://arxiv.org/abs/2303.08067
Autor:
Guirado, Robert, Perez-Palomino, Gerardo, Ferreras, Marta, Carrasco, Eduardo, Caño-García, Manuel
This paper describes and validates for the first time the dynamic modelling of Liquid Crystal (LC)-based planar multi-resonant cells, as well as its use as bias signals synthesis tool to improve their reconfigurability time. The dynamic LC director e
Externí odkaz:
http://arxiv.org/abs/2209.14597
Autor:
Guirado, Robert, Rahimi, Abbas, Karunaratne, Geethan, Alarcón, Eduard, Sebastian, Abu, Abadal, Sergi
Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using very long random vectors (aka hypervectors). Among different hardware platforms capable of executing HDC algorithms, in-memor
Externí odkaz:
http://arxiv.org/abs/2205.10889
Relational data present in real world graph representations demands for tools capable to study it accurately. In this regard Graph Neural Network (GNN) is a powerful tool, wherein various models for it have also been developed over the past decade. R
Externí odkaz:
http://arxiv.org/abs/2103.10515
Autor:
Garg, Raveesh, Qin, Eric, Muñoz-Martínez, Francisco, Guirado, Robert, Jain, Akshay, Abadal, Sergi, Abellán, José L., Acacio, Manuel E., Alarcón, Eduard, Rajamanickam, Sivasankaran, Krishna, Tushar
Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory charac
Externí odkaz:
http://arxiv.org/abs/2103.07977
Deep neural network (DNN) models continue to grow in size and complexity, demanding higher computational power to enable real-time inference. To efficiently deliver such computational demands, hardware accelerators are being developed and deployed ac
Externí odkaz:
http://arxiv.org/abs/2011.14755
Autor:
Abadal, Sergi, Guirado, Robert, Taghvaee, Hamidreza, Jain, Akshay, de Santana, Elana Pereira, Bolívar, Peter Haring, Saeed, Mohamed, Negra, Renato, Wang, Zhenxing, Wang, Kun-Ta, Lemme, Max C., Klein, Joshua, Zapater, Marina, Levisse, Alexandre, Atienza, David, Rossi, Davide, Conti, Francesco, Dazzi, Martino, Karunaratne, Geethan, Boybat, Irem, Sebastian, Abu
Publikováno v:
IEEE Wireless Communications Magazine, vol. 30, no. 4, pp. 162-169, 2023
The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly co-integrate multiple specialized processor and memory chiplets. In suc
Externí odkaz:
http://arxiv.org/abs/2011.04107
Publikováno v:
ACM Computing Surveys, 2021
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inhe
Externí odkaz:
http://arxiv.org/abs/2010.00130
Deep Neural Networks have flourished at an unprecedented pace in recent years. They have achieved outstanding accuracy in fields such as computer vision, natural language processing, medicine or economics. Specifically, Convolutional Neural Networks
Externí odkaz:
http://arxiv.org/abs/1912.01664
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