Algorithms and Data Structures for Numerical Computations with Automatic Precision Estimation
Igor V. Netay
AIgor V. Netay, JSRPC Kryptonite and Institute for Information Transmission Problems of Russian Academy of Sciences, Moscow, Russia.
Manuscript received on 07 October 2024 | Revised Manuscript received on 11 October 2024 | Manuscript Accepted on 15 October 2024 | Manuscript published on 30 October 2024 | PP: 19-24 | Volume-4 Issue-6, October 2024 | Retrieval Number: 100.1/ijainn.F109204061024 | DOI: 10.54105/ijainn.F1092.04061024
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We introduce data structures and algorithms to count numerical inaccuracies arising from usage of floating numbers described in IEEE 754. Here we describe how to estimate precision for some collection of functions most commonly used for array manipulations and training of neural networks. For highly optimized functions like matrix multiplication, we provide a fast estimation of precision and some hint how the estimation can be strengthened.
Keywords: Digital Noise; Floating Point Numbers, Neural Networks, Numerical Analysis.
Scope of the Article: Machine Learning Algorithms