UID:
almahu_9949335244602882
Umfang:
VII, 135 p. 47 illus., 34 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2022.
ISBN:
9783031097799
Serie:
Lecture Notes in Computer Science, 13253
Inhalt:
This book constitutes the refereed proceedings of the Third International Conference on Next Generation Arithmetic, CoNGA 2022, which was held in Singapore, during March 1-3, 2022. The 8 full papers included in this book were carefully reviewed and selected from 12 submissions. They deal with emerging technologies for computer arithmetic focusing on the demands of both AI and high-performance computing. .
Anmerkung:
On the Implementation of Edge Detection Algorithms with SORN Arithmetic -- A Posit8 Decompression Operator for Deep Neural Network Inference -- Qtorch+: Next Generation Arithmetic for Pytorch Machine Learning -- ACTION: Automated Hardware-Software Codesign Framework for Low-precision Numerical Format SelecTION in TinyML -- MultiPosits: Universal Coding of Rn -- Comparing Different Decodings for Posit Arithmetic -- Universal⋆: Reliable, Reproducible, and Energy-Efficient Numerics -- Small reals representations for Deep Learning at the edge: a comparison.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031097782
Weitere Ausg.:
Printed edition: ISBN 9783031097805
Sprache:
Englisch
DOI:
10.1007/978-3-031-09779-9
URL:
https://doi.org/10.1007/978-3-031-09779-9
