Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Castrillon, Jeronimo"'
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:
Schütze, Lars, Castrillon, Jeronimo
Adaptive software becomes more and more important as computing is increasingly context-dependent. Runtime adaptability can be achieved by dynamically selecting and applying context-specific code. Role-oriented programming has been proposed as a parad
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A73183
https://tud.qucosa.de/api/qucosa%3A73183/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A73183/attachment/ATT-0/
Autor:
Schütze, Lars, Castrillon, Jeronimo
Adaptive software becomes more and more important as computing is increasingly context-dependent. Runtime adaptability can be achieved by dynamically selecting and applying context-specific code. Role-oriented programming has been proposed as a parad
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A73178
https://tud.qucosa.de/api/qucosa%3A73178/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A73178/attachment/ATT-0/
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:
Wolfgang, Lehner, Fettweis, Gerhard P., Dörpinghaus, Meik, Castrillon, Jeronimo, Kumar, Akash, Baier, Christel, Bock, Karlheinz, Ellinger, Frank, Fery, Andreas, Fitzek, Frank H. P., Härtig, Hermann, Jamshidi, Kambiz, Kissinger, Thomas, Mertig, Michael, Nagel, Wolfgang E., Nguyen, Giang T., Plettemeier, Dirk, Schröter, Michael, Strufe, Thorsten
With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose t
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A82183
https://tud.qucosa.de/api/qucosa%3A82183/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A82183/attachment/ATT-0/