Abstract
High performance implementations of trigonometric and hyperbolic functions are important in many application areas. This paper presents an FPGA implementation of these functions using the stochastic computing methodology. The results are compared to the well-known CORDIC approach. All of the designs are synthesized and implemented on a Xilinx Virtex-5 FPGA. The results are compared in terms of delay and area for various input data widths. The results show that the proposed design method has advantages in the delay-area product and in soft error tolerance compared to CORDIC designs.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017 |
| Editors | Yajie Qin, Zhiliang Hong, Ting-Ao Tang |
| Publisher | IEEE Computer Society |
| Pages | 553-556 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509066247 |
| DOIs | |
| State | Published - Jul 1 2017 |
| Event | 12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017 - Guiyang, China Duration: Oct 25 2017 → Oct 28 2017 |
Publication series
| Name | Proceedings of International Conference on ASIC |
|---|---|
| Volume | 2017-October |
| ISSN (Print) | 2162-7541 |
| ISSN (Electronic) | 2162-755X |
Other
| Other | 12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017 |
|---|---|
| Country/Territory | China |
| City | Guiyang |
| Period | 10/25/17 → 10/28/17 |
Bibliographical note
Funding Information:This work was supported in part by the National Science Foundation (grant numbers CCF-1241987 and CCF-1408123 $Q\ RSLQLRQV ¿QGLQJV DQG FRQFOXVLRQV or recommendations expressed in this material are those RI WKH DXWKRUV DQG GR QRW QHFHVVDULO\ UHÀHFW WKH YLHZV RI the NSF. This work is also supported in part by the Minnesota Supercomputing Institute, by a donation from NVIDIA, and by State Key Lab of ASIC & System, grant number 2016GF010. Peng Li was with the Univ. of Minnesota when this work was performed.
Funding Information:
This work was supported in part by the National Science Foundation (grant numbers CCF-1241987 and CCF-1408123). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authros and do not necessarily reflect the views of the NSF. This work is also supported in part by the Minnesota Supercomputing Institute, by a donation from NVIDIA, and by State Key Lab of ASIC and System, grant number 2016GF010. Peng Li was with the Univ. of Minnesota when this work was performed
Publisher Copyright:
© 2017 IEEE.