Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Alvin R. Lebeck"'
Publikováno v:
IEEE Micro. 43:76-82
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39f84dd2248bc7bf770fface64eb983f
https://doi.org/10.1109/mm.2022.3208746
https://doi.org/10.1109/mm.2022.3208746
Autor:
John Snyder, Alvin R. Lebeck
Publikováno v:
2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3dfd39d00be012b3437d8cb93fc1db4
https://doi.org/10.1109/ipdps53621.2022.00102
https://doi.org/10.1109/ipdps53621.2022.00102
Publikováno v:
ASPLOS
Statistical machine learning often uses probabilistic models and algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Probabilistic computations, often considered too slow on conventional processors, can be accelera
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab4b39bde41fa45a68da002799a2b92a
https://doi.org/10.1145/3445814.3446697
https://doi.org/10.1145/3445814.3446697
Publikováno v:
IEEE Micro. 37:52-62
As lithographic feature sizes approach fundamental scaling limits, a variety of computational domains remain incompatible with integrated circuits merely due to their operating principles. Resonance energy transfer (RET) logic offers a molecular-scal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5bb6059f1caffc6fa51ee46bd68b74f
https://doi.org/10.1109/mm.2017.3211112
https://doi.org/10.1109/mm.2017.3211112
Publikováno v:
Job Scheduling Strategies for Parallel Processing ISBN: 9783030106317
JSSPP
JSSPP
Graphics Processing Units (GPUs) are energy-efficient massively parallel accelerators that are increasingly deployed in multi-tenant environments such as data-centers for general-purpose computing as well as graphics applications. Using GPUs in multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2347d5869a417707c37e117dfada6c57
https://doi.org/10.1007/978-3-030-10632-4_5
https://doi.org/10.1007/978-3-030-10632-4_5
Autor:
Ramin Bashizade, Siyang Wang, Yuxuan Li, Alvin R. Lebeck, Xiangyu Zhang, Chris Dwyer, Song Yang
Publikováno v:
ISCA
The increasing use of probabilistic algorithms from statistics and machine learning for data analytics presents new challenges and opportunities for the design of computing systems. One important class of probabilistic machine learning algorithms is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eef3f2591badc63bda59620bc60c2bb6
https://doi.org/10.1145/3007787.3001196
https://doi.org/10.1145/3007787.3001196
Publikováno v:
PPOPP
Similarity search finds the most similar matches in an object collection for a given query; making it an important problem across a wide range of disciplines such as web search, image recognition and protein sequencing. Practical implementations of H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b3a1d8310dd1015e09917e8ac864e58
https://doi.org/10.1145/3016078.2851144
https://doi.org/10.1145/3016078.2851144
Publikováno v:
MobiSys
The most promising way to improve the performance of dynamic information-flow tracking (DIFT) for machine code is to only track instructions when they process tainted data. Unfortunately, prior approaches to on-demand DIFT are a poor match for modern
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de37a6e67bd5df87b0034a3a43cd1560
https://doi.org/10.1145/3210240.3210321
https://doi.org/10.1145/3210240.3210321
Publikováno v:
ISCA
The increasing difficulty in leveraging CMOS scaling for improved performance requires exploring alternative technologies. A promising technique is to exploit the physical properties of devices to specialize certain computations. A recently proposed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd91d2a718fa2fd58157afe0cf2b9375
https://doi.org/10.1109/isca.2018.00034
https://doi.org/10.1109/isca.2018.00034
Publikováno v:
IEEE Micro. 35:72-84
Despite the theoretical advances in probabilistic computing, a fundamental mismatch persists between the deterministic hardware that traditional computers use and the stochastic nature of probabilistic algorithms. In this article, the authors propose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e7033ca5d89de9bc27c7701ec7309fe
https://doi.org/10.1109/mm.2015.124
https://doi.org/10.1109/mm.2015.124