Paper Info
Title
Adaptive segmentation methodology for hardware function evaluators.
Abstract
This paper presents a new adaptive function segmentation methodology to evaluate mathematical functions in hardware systems through piece-wise polynomial approximation methods. In contrast to conventional segmentation techniques, this methodology automatically adjusts the segmentation strategy through a function shape analysis based on the first- and second-order derivatives. Additionally, a particle swarm optimization algorithm is implemented to search for the best segmentation parameters that satisfy the designer-given signal-to-quantization-noise ratio specification and minimize the number of polynomials. The main advantages are a significant lookup table size reduction, increased approximation accuracy of highly nonlinear sections, and the automatic generation of a hierarchy-less segmentation solution. Hence, the proposed methodology enables efficient development of hardware accelerated surrogate models such as wireless channel emulators and other signal processing applications on inexpensive platforms that rely on fixed-point number representation as a compromise between performance, and output accuracy.
Year
DOI
Venue
2018
10.1016/j.compeleceng.2018.04.024
Computers & Electrical Engineering
Keywords
Field
DocType
Function approximation,Piecewise polynomial,Hardware optimization,Polynomial segmentation,Mathematical functions,Function evaluators,Surrogate modeling,Particle swarm optimization
Particle swarm optimization,Lookup table,Signal processing,Nonlinear system,Function (mathematics),Polynomial,Computer science,Segmentation,Communication channel,Computer hardware
Journal
Volume
ISSN
Citations 
69
0045-7906
0
PageRank 
References 
Authors
0.34
13
6
Name
Order
Citations
PageRank
J. M. Trejo-Arellano100.34
J. Vazquez Castillo284.59
Omar Humberto Longoria-Gandara3466.30
Roberto Carrasco-Alvarez4285.80
C. A. Gutierrez5119.84
Alejandro Castillo Atoche6135.25