Publication

Cracking The Complexity Of Fixed-Point Refinement In Complex Wireless Systems

Paolo Ienne, David Novo Bruna
2013
Conference Papers

Abstract

Fixed-point arithmetic leads to efficient implementations. However, the optimization process required to size each of the implementation signals can be prohibitively complex. In this paper, we introduce a new divide-and-conquer method that is able to approach the quality of global methods in significantly less time. Firstly, our method sorts the signals in multiple groups considering the propagation path of the signals to the global application metric (i.e., bit-error rate). Then, the fixed-point configurations of the groups are resolved with fast local simulations. Finally, the global fixed-point configuration is composed by the group configurations using slow global simulations. The method is applied to the fixed-point refinement of an advanced wireless algorithm achieving close to 9 times speedup with respect to a reference statistical method without affecting the quality of the result.

Official source

https://infoscience.epfl.ch/entities/publication/5027189a-87c2-4f13-9e4b-8126f8bd9d5e

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Paolo Ienne, David Novo Bruna
2013
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/5027189a-87c2-4f13-9e4b-8126f8bd9d5e

About this result

Related concepts (32)

Euler method

In mathematics and computational science, the Euler method (also called the forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It is the most basic explicit method for numerical integration of ordinary differential equations and is the simplest Runge–Kutta method. The Euler method is named after Leonhard Euler, who first proposed it in his book Institutionum calculi integralis (published 1768–1870).

Floating-point arithmetic

In computing, floating-point arithmetic (FP) is arithmetic that represents subsets of real numbers using an integer with a fixed precision, called the significand, scaled by an integer exponent of a fixed base. Numbers of this form are called floating-point numbers. For example, 12.345 is a floating-point number in base ten with five digits of precision: However, unlike 12.345, 12.3456 is not a floating-point number in base ten with five digits of precision—it needs six digits of precision; the nearest floating-point number with only five digits is 12.

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In computer science, merge sort (also commonly spelled as mergesort) is an efficient, general-purpose, and comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the relative order of equal elements is the same in the input and output. Merge sort is a divide-and-conquer algorithm that was invented by John von Neumann in 1945. A detailed description and analysis of bottom-up merge sort appeared in a report by Goldstine and von Neumann as early as 1948.

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