Enabling Heterogeneous, Multicore SoC Research with RISC-V and ESP

Autor: Zuckerman, Joseph, Mantovani, Paolo, Giri, Davide, Carloni, Luca P.

Heterogeneous, multicore SoC architectures are a critical component of today's computing landscape. However, supporting both increasing heterogeneity and multicore execution are significant design challenges. Meanwhile, the growing RISC-V and open-so

Externí odkaz: http://arxiv.org/abs/2206.01901

Cohmeleon: Learning-Based Orchestration of Accelerator Coherence in Heterogeneous SoCs

Autor: Zuckerman, Joseph, Giri, Davide, Kwon, Jihye, Mantovani, Paolo, Carloni, Luca P.

One of the most critical aspects of integrating loosely-coupled accelerators in heterogeneous SoC architectures is orchestrating their interactions with the memory hierarchy, especially in terms of navigating the various cache-coherence options: from

Externí odkaz: http://arxiv.org/abs/2109.06382

The MosaicSim Simulator (Full Technical Report)

Autor: Matthews, Opeoluwa, Manocha, Aninda, Giri, Davide, Orenes-Vera, Marcelo, Tureci, Esin, Sorensen, Tyler, Ham, Tae Jun, Aragón, Juan L., Carloni, Luca P., Martonosi, Margaret

As Moore's Law has slowed and Dennard Scaling has ended, architects are increasingly turning to heterogeneous parallelism and domain-specific hardware-software co-designs. These trends present new challenges for simulation-based performance assessmen

Externí odkaz: http://arxiv.org/abs/2004.07415

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Applications and Techniques for Fast Machine Learning in Science

Autor: Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bahr, Steffen, Becker, Jurgen, Berthold, Anne-Sophie, Bonventre, Richard J., Bravo, Tomas E. Muller, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Guo, Dongning, Hazelwood, Kyle J., Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, Krosigk, Belinavon, von Krosigk, Belina, Wang, Shen, Warburton, Thomas K., Flechas, Maria Acosta, Aportela, Anthony, Calvet, Thomas, Cristella, Leonardo, Diaz, Daniel, Doglioni, Caterina, Galati, Maria Domenica, Khoda, Elham E., Fahim, Farah, Giri, Davide, Hawks, Benjamin, Hoang, Duc, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Johnson, Iris, Kansal, Raghav, Kastner, Ryan, Katsavounidis, Erik, Krupa, Jeffrey, Li, Pan, Madireddy, Sandeep, Marx, Ethan, McCormack, Patrick, Meza, Andres, Mitrevski, Jovan, Mohammed, Mohammed Attia, Mokhtar, Farouk, Moreno, Eric, Nagu, Srishti, Narayan, Rohin, Palladino, Noah, Que, Zhiqiang, Park, Sang Eon, Ramamoorthy, Subramanian, Rankin, Dylan, Rothman, Simon, Sharma, Ashish, Summers, Sioni, Vischia, Pietro, Vlimant, Jean-Roch, Weng, Olivia

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b303f375dc1d161d8c2ba838be2be550