Low parametric closed-loop sensitivity realizations using fixed-point and floating-point arithmetic

This paper discusses the advantages of using fixed-point arithmetic in a hybrid environment. The particular ex ample used is that of a hybrid simulation system. The paper discusses the performance penalties which are paid in using floating-point arithmetic as opposed to fixed-point arithmetic. The paper points out the amount of digital- computer time used in fixed-point to floating-point con version. It also suggests a new hardware device, a floating point/fixed-point digital-to-analog converter. This paper points out one application in which this device could be useful.

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Efficient Emulation of Floating-Point Arithmetic on Fixed-Point SIMD Processors

2016 IEEE International Workshop on Signal Processing Systems (SiPS) ◽

10.1109/sips.2016.52 ◽

2016 ◽

Cited By ~ 2

Author(s):

Lukas Gerlach ◽

Guillermo Paya-Vaya ◽

Holger Blume

Keyword(s):

Fixed Point ◽

Floating Point ◽

Floating Point Arithmetic ◽

Simd Processors ◽

Point Arithmetic

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Customizing fixed-point and floating-point arithmetic — A case study in K-means clustering

2017 IEEE International Workshop on Signal Processing Systems (SiPS) ◽

10.1109/sips.2017.8109980 ◽

2017 ◽

Cited By ~ 4

Author(s):

Benjamin Barrois ◽

Olivier Sentieys

Keyword(s):

Fixed Point ◽

Floating Point ◽

Floating Point Arithmetic ◽

Point Arithmetic

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Specification for binary floating point arithmetic for microprocessor systems

10.3403/00218955u ◽

2015 ◽

Keyword(s):

Floating Point ◽

Floating Point Arithmetic ◽

Point Arithmetic

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Numerical algorithms for high-performance computational science

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2019.0066 ◽

2020 ◽

Vol 378 (2166) ◽

pp. 20190066 ◽

Cited By ~ 2

Author(s):

Jack Dongarra ◽

Laura Grigori ◽

Nicholas J. Higham

Keyword(s):

High Performance ◽

Numerical Algorithms ◽

Computational Science ◽

Floating Point ◽

Important Criterion ◽

Data Movement ◽

Floating Point Arithmetic ◽

High Performance Computers ◽

Point Arithmetic ◽

Speed And Accuracy

A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

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