Name
Abstract | ||
|---|---|---|
Automated design of two-variable numeric functions can be realized efficiently by extending well-known multiplier-less linear function approximation techniques; the arithmetic signal processing effort is minimized by the utilization of a non-uniform piecewise segmentation scheme. However, as common state-of-the-art approaches only consider unpretentious coefficient estimation techniques, such as gradient superposition, this results in large multiplexer-trees for segmentation that, consequently, are restricting the total performance. In this paper a least-squares-based estimation of multiplier-less linear coefficients is introduced that minimizes the number of segments by using a least-squares-based coefficient estimation. The evaluation indicates a reduction of the segmentation effort by nearly 31% on average. Logical and physical CMOS synthesis is performed and the results are compared to actual references highlighting our work high performance approach for the hardware-based calculation of two-variable numeric functions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NORCHIP.2015.7364413 | 2015 Nordic Circuits and Systems Conference (NORCAS): NORCHIP & International Symposium on System-on-Chip (SoC) |
Keywords | Field | DocType |
numeric function approximation,two-variable,multiplier-less,least-squares | Least squares,Approximation algorithm,Signal processing,Mathematical optimization,Superposition principle,Function approximation,Segmentation,Computer science,Parallel computing,Algorithm,Linear function,Piecewise | Conference |
Citations | PageRank | References |
1 | 0.37 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jochen Rust | 1 | 32 | 12.51 |
| Nils Heidmann | 2 | 14 | 3.89 |
| Steffen Paul | 3 | 142 | 40.96 |