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Auswahl der wissenschaftlichen Literatur zum Thema „Stochastic rounding“
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Zeitschriftenartikel zum Thema "Stochastic rounding"
Paxton, E. Adam, Matthew Chantry, Milan Klöwer, Leo Saffin und Tim Palmer. „Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding“. Journal of Climate 35, Nr. 4 (15.02.2022): 1215–29. http://dx.doi.org/10.1175/jcli-d-21-0343.1.
Der volle Inhalt der QuelleConnolly, Michael P., Nicholas J. Higham und Theo Mary. „Stochastic Rounding and Its Probabilistic Backward Error Analysis“. SIAM Journal on Scientific Computing 43, Nr. 1 (Januar 2021): A566—A585. http://dx.doi.org/10.1137/20m1334796.
Der volle Inhalt der QuelleGupta, Anupam, R. Ravi und Amitabh Sinha. „LP Rounding Approximation Algorithms for Stochastic Network Design“. Mathematics of Operations Research 32, Nr. 2 (Mai 2007): 345–64. http://dx.doi.org/10.1287/moor.1060.0237.
Der volle Inhalt der QuelleArciniega, Armando, und Edward Allen. „Rounding Error in Numerical Solution of Stochastic Differential Equations“. Stochastic Analysis and Applications 21, Nr. 2 (04.01.2003): 281–300. http://dx.doi.org/10.1081/sap-120019286.
Der volle Inhalt der QuelleArar, El-Mehdi El, Devan Sohier, Pablo de Oliveira Castro und Eric Petit. „Stochastic Rounding Variance and Probabilistic Bounds: A New Approach“. SIAM Journal on Scientific Computing 45, Nr. 5 (05.10.2023): C255—C275. http://dx.doi.org/10.1137/22m1510819.
Der volle Inhalt der QuelleMcCarl, Bruce A. „Generalized Stochastic Dominance: An Empirical Examination“. Journal of Agricultural and Applied Economics 22, Nr. 2 (Dezember 1990): 49–55. http://dx.doi.org/10.1017/s1074070800001796.
Der volle Inhalt der QuelleHopkins, Michael, Mantas Mikaitis, Dave R. Lester und Steve Furber. „Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations“. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, Nr. 2166 (20.01.2020): 20190052. http://dx.doi.org/10.1098/rsta.2019.0052.
Der volle Inhalt der QuelleJi, Sai, Dachuan Xu, Donglei Du und Yijing Wang. „LP-rounding approximation algorithms for two-stage stochastic fault-tolerant facility location problem“. Applied Mathematical Modelling 58 (Juni 2018): 76–85. http://dx.doi.org/10.1016/j.apm.2017.12.009.
Der volle Inhalt der QuelleTovissodé, Chénangnon Frédéric, Sèwanou Hermann Honfo, Jonas Têlé Doumatè und Romain Glèlè Kakaï. „On the Discretization of Continuous Probability Distributions Using a Probabilistic Rounding Mechanism“. Mathematics 9, Nr. 5 (06.03.2021): 555. http://dx.doi.org/10.3390/math9050555.
Der volle Inhalt der QuelleЧубич, Владимир Михайлович, und Светлана Олеговна Кулабухова. „Square-root algorithms for robust modifications of the continuous-discrete cubature Kalman filter“. Вычислительные технологии, Nr. 3 (15.07.2020): 88–98. http://dx.doi.org/10.25743/ict.2020.25.3.010.
Der volle Inhalt der QuelleDissertationen zum Thema "Stochastic rounding"
El, Arar El-Mehdi. „Stochastic models for the evaluation of numerical errors“. Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG104.
Der volle Inhalt der QuelleThe idea of assuming rounding errors as random variables is not new. Based on tools such as independent random variables or the Central Limit Theorem, various propositions have demonstrated error bounds in O(√n). This thesis is dedicated to studying stochastic rounding (SR) as a replacement for the default deterministic rounding mode. First, we introduce a new approach to derive a probabilistic error bound in O(√n) based on variance calculation and Bienaymé-Chebyshev inequality. Second, we demonstrate a general framework that allows the probabilistic error analysis of algorithms under SR. In this context, we decompose the error into a martingale plus a drift. We show that the drift is zero for algorithms with multi-linear errors, while the probabilistic analysis of the martingale term leads to probabilistic error bounds in O(√n). We show that the drift is negligible at the first order compared to the martingale term for the variance computation, and we prove probabilistic error bounds in O(√n)
Picot, Romain. „Amélioration de la fiabilité numérique de codes de calcul industriels“. Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS242.
Der volle Inhalt der QuelleMany studies are devoted to performance of numerical simulations. However it is also important to take into account the impact of rounding errors on the results produced. These rounding errors can be estimated with Discrete Stochastic Arithmetic (DSA), implemented in the CADNA library. Compensated algorithms improve the accuracy of results, without changing the numerical types used. They have been designed to be generally executed with rounding to nearest. We have established error bounds for these algorithms with directed rounding and shown that they can be used successfully with the random rounding mode of DSA. We have also studied the impact of a target precision of the results on the numerical types of the different variables. We have developed the PROMISE tool which automatically performs these type changes while validating the results thanks to DSA. The PROMISE tool has thus provided new configurations of types combining single and double precision in various programs and in particular in the MICADO code developed at EDF. We have shown how to estimate with DSA rounding errors generated in quadruple precision. We have proposed a version of CADNA that integrates quadruple precision and that allowed us in particular to validate the computation of multiple roots of polynomials. Finally we have used this new version of CADNA in the PROMISE tool so that it can provide configurations with three types (single, double and quadruple precision)
Huber, Anna [Verfasser]. „Randomized rounding and rumor spreading with stochastic dependencies / vorgelegt von Anna Huber“. 2010. http://d-nb.info/1008296163/34.
Der volle Inhalt der QuelleBuchteile zum Thema "Stochastic rounding"
Giessing, Sarah. „Flexible Rounding Based on Consistent Post-tabular Stochastic Noise“. In Privacy in Statistical Databases, 22–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33627-0_3.
Der volle Inhalt der QuelleYuan, Geng, Sung-En Chang, Qing Jin, Alec Lu, Yanyu Li, Yushu Wu, Zhenglun Kong et al. „You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding“. In Lecture Notes in Computer Science, 34–51. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_3.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Stochastic rounding"
Mikaitis, Mantas. „Stochastic Rounding: Algorithms and Hardware Accelerator“. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533756.
Der volle Inhalt der QuelleChang, Sung-En, Geng Yuan, Alec Lu, Mengshu Sun, Yanyu Li, Xiaolong Ma, Zhengang Li et al. „Hardware-efficient stochastic rounding unit design for DNN training“. In DAC '22: 59th ACM/IEEE Design Automation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3489517.3530619.
Der volle Inhalt der QuelleArar, El-Mehdi El, Devan Sohier, Pablo de Oliveira Castro und Eric Petit. „The Positive Effects of Stochastic Rounding in Numerical Algorithms“. In 2022 IEEE 29th Symposium on Computer Arithmetic (ARITH). IEEE, 2022. http://dx.doi.org/10.1109/arith54963.2022.00018.
Der volle Inhalt der QuelleQian Zhang, Sai, Bradley McDanel und H. T. Kung. „FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding“. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 2022. http://dx.doi.org/10.1109/hpca53966.2022.00067.
Der volle Inhalt der QuelleEssam, Mohaned, Tong Boon Tang, Eric Tatt Wei Ho und Hsin Chen. „Dynamic point stochastic rounding algorithm for limited precision arithmetic in Deep Belief Network training“. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2017. http://dx.doi.org/10.1109/ner.2017.8008430.
Der volle Inhalt der QuelleChang, Sung-En, Geng Yuan, Alec Lu, Mengshu Sun, Yanyu Li, Xiaolong Ma, Zhengang Li et al. „ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for Low-Bit DNN Training“. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2023. http://dx.doi.org/10.23919/date56975.2023.10137222.
Der volle Inhalt der QuelleGhenaiet, Adel. „Study of Sand Particle Trajectories and Erosion Into the First Fan Stage of a Turbofan“. In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22415.
Der volle Inhalt der QuelleDodson, C. T. J., und W. W. Sampson. „Effect of Correlated Free Fibre Lengths on Pore Size Distribution in Fibrous Mats“. In Advances in Paper Science and Technology, herausgegeben von S. J. I’Anson. Fundamental Research Committee (FRC), Manchester, 2005. http://dx.doi.org/10.15376/frc.2005.2.943.
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