.png){kind=icon}
Return
Adaptive segmentation methodology for hardware function evaluators
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2018.04.024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Function approximation
Piecewise polynomial
Hardware optimization
Polynomial segmentation
Mathematical functions
Function evaluators
Surrogate modeling
Particle swarm optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.9
Papers:
6.7K
Citations:
1.3W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSave View more