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Next Generation Arithmetic : Third International Conference, CoNGA 2022, Singapore, March 1–3, 2022, Revised Selected Papers / edited by John Gustafson, Vassil Dimitrov.
SpringerLink Books Lecture Notes In Computer Science (LNCS) (1997-2024) Available online
SpringerLink Books Lecture Notes In Computer Science (LNCS) (1997-2024)- Format:
- Book
- Series:
- Lecture Notes in Computer Science, 1611-3349 ; 13253
- Language:
- English
- Subjects (All):
- Computer arithmetic and logic units.
- Coding theory.
- Information theory.
- Microprogramming.
- Computer input-output equipment.
- Computer networks.
- Computer science--Mathematics.
- Computer science.
- Arithmetic and Logic Structures.
- Coding and Information Theory.
- Control Structures and Microprogramming.
- Input/Output and Data Communications.
- Computer Communication Networks.
- Mathematics of Computing.
- Local Subjects:
- Arithmetic and Logic Structures.
- Coding and Information Theory.
- Control Structures and Microprogramming.
- Input/Output and Data Communications.
- Computer Communication Networks.
- Mathematics of Computing.
- Physical Description:
- 1 online resource (142 pages)
- Edition:
- 1st ed. 2022.
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2022.
- Summary:
- 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. .
- Contents:
- 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.
- Other Format:
- Print version: Gustafson, John Next Generation Arithmetic
- ISBN:
- 3-031-09779-3
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