Description

Book Synopsis
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.


Table of 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.

Next Generation Arithmetic: Third International

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    A Paperback / softback by John Gustafson, Vassil Dimitrov

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      Publisher: Springer International Publishing AG
      Publication Date: 14/07/2022
      ISBN13: 9783031097782, 978-3031097782
      ISBN10: 3031097785
      Also in:
      Computer hardware Coding theory and cryptology Information theory

      Description

      Book Synopsis
      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.


      Table of 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.

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