Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Castrillón, Jerónimo"'
Autor:
de Lima, João Paulo Cardoso, Morris III, Benjamin Franklin, Khan, Asif Ali, Castrillon, Jeronimo, Jones, Alex K.
Big data processing has exposed the limits of compute-centric hardware acceleration due to the memory-to-processor bandwidth bottleneck. Consequently, there has been a shift towards memory-centric architectures, leveraging substantial compute paralle
Externí odkaz:
http://arxiv.org/abs/2409.10136
Energy efficiency has become a key concern in modern computing. Major processor vendors now offer heterogeneous architectures that combine powerful cores with energy-efficient ones, such as Intel P/E systems, Apple M1 chips, and Samsungs Exyno's CPUs
Externí odkaz:
http://arxiv.org/abs/2406.18980
Autor:
Lin, Shaokai, Jellum, Erling, Theile, Mirco, Tanneberger, Tassilo, Sun, Binqi, Jerad, Chadlia, Xu, Ruomu, Feng, Guangyu, Menard, Christian, Lohstroh, Marten, Castrillon, Jeronimo, Seshia, Sanjit, Lee, Edward
This paper introduces the Precision-Timed Virtual Machine (PretVM), an intermediate platform facilitating the execution of quasi-static schedules compiled from a subset of programs written in the Lingua Franca (LF) coordination language. The subset c
Externí odkaz:
http://arxiv.org/abs/2406.06253
Autor:
Pilato, Christian, Banik, Subhadeep, Beranek, Jakub, Brocheton, Fabien, Castrillon, Jeronimo, Cevasco, Riccardo, Cmar, Radim, Curzel, Serena, Ferrandi, Fabrizio, Friebel, Karl F. A., Galizia, Antonella, Grasso, Matteo, Silva, Paulo, Martinovic, Jan, Palermo, Gianluca, Paolino, Michele, Parodi, Andrea, Parodi, Antonio, Pintus, Fabio, Polig, Raphael, Poulet, David, Regazzoni, Francesco, Ringlein, Burkhard, Rocco, Roberto, Slaninova, Katerina, Slooff, Tom, Soldavini, Stephanie, Suchert, Felix, Tibaldi, Mattia, Weiss, Beat, Hagleitner, Christoph
Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly e
Externí odkaz:
http://arxiv.org/abs/2402.12612
In today's data-centric world, where data fuels numerous application domains, with machine learning at the forefront, handling the enormous volume of data efficiently in terms of time and energy presents a formidable challenge. Conventional computing
Externí odkaz:
http://arxiv.org/abs/2401.14428
The accuracy of neural networks has greatly improved across various domains over the past years. Their ever-increasing complexity, however, leads to prohibitively high energy demands and latency in von Neumann systems. Several computing-in-memory (CI
Externí odkaz:
http://arxiv.org/abs/2401.12630
Privacy-preserving analysis of confidential data can increase the value of such data and even improve peoples' lives. Fully homomorphic encryption (FHE) can enable privacy-preserving analysis. However, FHE adds a large amount of computational overhea
Externí odkaz:
http://arxiv.org/abs/2312.14250
Autor:
Farzaneh, Hamid, de Lima, João Paulo Cardoso, Li, Mengyuan, Khan, Asif Ali, Hu, Xiaobo Sharon, Castrillon, Jeronimo
Machine learning and data analytics applications increasingly suffer from the high latency and energy consumption of conventional von Neumann architectures. Recently, several in-memory and near-memory systems have been proposed to remove this von Neu
Externí odkaz:
http://arxiv.org/abs/2309.06418
Autor:
Menard, Christian, Lohstroh, Marten, Bateni, Soroush, Chorlian, Matthew, Deng, Arthur, Donovan, Peter, Fournier, Clément, Lin, Shaokai, Suchert, Felix, Tanneberger, Tassilo, Kim, Hokeun, Castrillon, Jeronimo, Lee, Edward A.
Actor frameworks and similar reactive programming techniques are widely used for building concurrent systems. They promise to be efficient and scale well to a large number of cores or nodes in a distributed system. However, they also expose programme
Externí odkaz:
http://arxiv.org/abs/2301.02444
Autor:
Khan, Asif Ali, Farzaneh, Hamid, Friebel, Karl F. A., Fournier, Clément, Chelini, Lorenzo, Castrillon, Jeronimo
The rise of data-intensive applications exposed the limitations of conventional processor-centric von-Neumann architectures that struggle to meet the off-chip memory bandwidth demand. Therefore, recent innovations in computer architecture advocate co
Externí odkaz:
http://arxiv.org/abs/2301.07486