Bibliografías temáticas / Stochastic rounding

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Literatura académica sobre el tema "Stochastic rounding"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Stochastic rounding"

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Winkler, Julian, and Sabah Badri-Hoeher. "Implementation of a Low-Cost Navigation System Using Data Fusion of a Micro-Electro-Mechanical System Inertial Sensor and an Ultra Short Baseline on a Microcontroller." Sensors 25, no. 10 (2025): 3125. https://doi.org/10.3390/s25103125.

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In this work, a low-cost low-power navigation solution for autonomous underwater vehicles is introduced utilizing a Micro-Electro-Mechanical System (MEMS) inertial sensor and an ultra short baseline (USBL) system. The complete signal processing is implemented on a cheap 16-bit fixed-point arithmetic microcontroller. For data fusion and calibration, an error state Kalman filter in square root form is used, which preserves stability in case of rounding errors. To further reduce the influence of rounding errors, a stochastic rounding scheme is applied. The USBL measurements are integrated using t
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Paxton, E. Adam, Matthew Chantry, Milan Klöwer, Leo Saffin, and Tim Palmer. "Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding." Journal of Climate 35, no. 4 (2022): 1215–29. http://dx.doi.org/10.1175/jcli-d-21-0343.1.

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Abstract Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model, which provides a stress test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system, a shallow water approximation for flow over a ridge, and a coarse-resolution spectral global atmospheric model with simplified parameterizations (SPEEDY). Although double precision [52 significant bits (sbits)] is standard across opera
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van Stee, Rob. "SIGACT News Online Algorithms Column 42." ACM SIGACT News 55, no. 3 (2024): 52–82. http://dx.doi.org/10.1145/3695887.3695894.

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For this issue, I am pleased to present a tutorial by Will Ma on using randomized rounding to design online policies. Randomized rounding has of course been used extensively in the design of approximation algorithms and also to approximately solve sequential stochastic optimization problems. Will describes several examples that show how randomized rounding can be used in this context. As always, I would like to invite more contributions to this column, be it surveys, conference reports, or technical articles related to online algorithms and competitive analysis. If you are considering becoming
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Connolly, Michael P., Nicholas J. Higham, and Theo Mary. "Stochastic Rounding and Its Probabilistic Backward Error Analysis." SIAM Journal on Scientific Computing 43, no. 1 (2021): A566—A585. http://dx.doi.org/10.1137/20m1334796.

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Gupta, Anupam, R. Ravi, and Amitabh Sinha. "LP Rounding Approximation Algorithms for Stochastic Network Design." Mathematics of Operations Research 32, no. 2 (2007): 345–64. http://dx.doi.org/10.1287/moor.1060.0237.

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Dexter, Gregory, Christos Boutsikas, Linkai Ma, Ilse C. F. Ipsen, and Petros Drineas. "Stochastic Rounding Implicitly Regularizes Tall-and-Thin Matrices." SIAM Journal on Matrix Analysis and Applications 46, no. 1 (2025): 341–69. https://doi.org/10.1137/24m1647679.

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Arciniega, Armando, and Edward Allen. "Rounding Error in Numerical Solution of Stochastic Differential Equations." Stochastic Analysis and Applications 21, no. 2 (2003): 281–300. http://dx.doi.org/10.1081/sap-120019286.

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Arar, El-Mehdi El, Devan Sohier, Pablo de Oliveira Castro, and Eric Petit. "Stochastic Rounding Variance and Probabilistic Bounds: A New Approach." SIAM Journal on Scientific Computing 45, no. 5 (2023): C255—C275. http://dx.doi.org/10.1137/22m1510819.

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McCarl, Bruce A. "Generalized Stochastic Dominance: An Empirical Examination." Journal of Agricultural and Applied Economics 22, no. 2 (1990): 49–55. http://dx.doi.org/10.1017/s1074070800001796.

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Abstract Use of generalized stochastic dominance (GSD) requires one to place lower and upper bounds on the risk aversion coefficient. This study showed that breakeven risk aversion coefficients found assuming the exponential utility function delineate the places where GSD preferences switch between prospects. However, between these break points, multiple, overlapping GSD intervals can be found. Consequently, when one does not have risk aversion coefficient information, discovery of breakeven coefficients instead of GSD use is recommended. The investigation also showed GSD results are insensiti
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10

El Arar, E.-M., D. Sohier, P. de Oliveira Castro, and E. Petit. "Bounds on Nonlinear Errors for Variance Computation with Stochastic Rounding." SIAM Journal on Scientific Computing 46, no. 5 (2024): B579—B599. http://dx.doi.org/10.1137/23m1563001.

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Tesis sobre el tema "Stochastic rounding"

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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.

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L'idée de considérer les erreurs d'arrondi comme des variables aléatoires n'est pas nouvelle. Basées sur des outils tels que l'indépendance des variables aléatoires ou le théorème central limite, plusieurs propositions ont démontré des bornes d'erreur en O(√n). Cette thèse est dédiée à l'étude de l'arrondi stochastique (SR) en tant que remplaçant du mode d'arrondi déterministe par défaut. Tout d'abord, nous introduisons une nouvelle approche pour dériver une borne probabiliste de l'erreur en O(√n), basée sur le calcul de la variance et l'inégalité de Bienaymé-Chebyshev. Ensuite, nous développo
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2

Picot, Romain. "Amélioration de la fiabilité numérique de codes de calcul industriels." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS242.

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De nombreux travaux sont consacrés à la performance des simulations numériques, or il est important de tenir compte aussi de l'impact des erreurs d'arrondi sur les résultats produits. Ces erreurs d'arrondi peuvent être estimées grâce à l'Arithmétique Stochastique Discrète (ASD), implantée dans la bibliothèque CADNA. Les algorithmes compensés permettent d'améliorer la précision des résultats, sans changer le type numérique utilisé. Ils ont été conçus pour être généralement exécutés en arrondi au plus près. Nous avons établi des bornes d'erreur pour ces algorithmes en arrondi dirigé et montré qu
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3

Huber, Anna [Verfasser]. "Randomized rounding and rumor spreading with stochastic dependencies / vorgelegt von Anna Huber." 2010. http://d-nb.info/1008296163/34.

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Capítulos de libros sobre el tema "Stochastic rounding"

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Giessing, Sarah. "Flexible Rounding Based on Consistent Post-tabular Stochastic Noise." In Privacy in Statistical Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33627-0_3.

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Yuan, Geng, Sung-En Chang, Qing Jin, et al. "You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_3.

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Gordon, Matthew K., and Michael Fiddler. "Vowel Harmony." In The Oxford Handbook of Vowel Harmony. Oxford University Press, 2024. http://dx.doi.org/10.1093/oxfordhb/9780198826804.013.46.

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Abstract Vowel harmony (VH) is a cross-linguistically pervasive phenomenon that ranges on a language-specific basis from a highly productive process to a vestigial property reflected in statistical patterns in the lexicon and morphologically restricted alternations. There are several types of VH that differ with respect to the phonological feature that spreads. The typologically most common VH systems operate along the front–back (palatal harmony), height, rounding (labial harmony), and tongue-root (advanced tongue root/restricted tongue root harmony) dimensions, although other varieties of VH
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Actas de conferencias sobre el tema "Stochastic rounding"

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Ali, Sami Ben, Silviu-Ioan Filip, and Olivier Sentieys. "A Stochastic Rounding-Enabled Low-Precision Floating-Point MAC for DNN Training." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546735.

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Jeong, Sangbeom, Seungil Lee, and Hyun Kim. "LowGradQ: Adaptive Gradient Quantization for Low-Bit CNN Training via Kernel Density Estimation-Guided Thresholding and Hardware-Efficient Stochastic Rounding Unit." In 2025 Design, Automation & Test in Europe Conference (DATE). IEEE, 2025. https://doi.org/10.23919/date64628.2025.10992985.

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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.

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Chang, Sung-En, Geng Yuan, Alec Lu, et al. "Hardware-efficient stochastic rounding unit design for DNN training." In DAC '22: 59th ACM/IEEE Design Automation Conference. ACM, 2022. http://dx.doi.org/10.1145/3489517.3530619.

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Arar, El-Mehdi El, Devan Sohier, Pablo de Oliveira Castro, and 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.

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Qian Zhang, Sai, Bradley McDanel, and 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.

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Jeong, Sangbeom, Dahun Choi, and Hyun Kim. "SRU-Q: Hardware-friendly Stochastic Rounding Unit-based Gradient Quantization for CNN Training." In 2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS). IEEE, 2024. http://dx.doi.org/10.1109/aicas59952.2024.10595985.

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Essam, Mohaned, Tong Boon Tang, Eric Tatt Wei Ho, and 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.

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9

Huang, Leran, Yongpan Liu, Xinyuan Lin, et al. "MPICC: Multiple-Precision Inter-Combined MAC Unit with Stochastic Rounding for Ultra-Low-Precision Training." In ASPDAC '25: 30th Asia and South Pacific Design Automation Conference. ACM, 2025. https://doi.org/10.1145/3658617.3697641.

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Chang, Sung-En, Geng Yuan, Alec Lu, 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.

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