Stochastic computing implementation of trigonometric and hyperbolic functions

Lian Huai, Peng Li, Gerald E. Sobelman, David J. Lilja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017
EditorsYajie Qin, Zhiliang Hong, Ting-Ao Tang
PublisherIEEE Computer Society
Pages553-556
Number of pages4
ISBN (Electronic)9781509066247
DOIs
StatePublished - Jul 1 2017
Event12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017 - Guiyang, China
Duration: Oct 25 2017Oct 28 2017

Publication series

NameProceedings of International Conference on ASIC
Volume2017-October
ISSN (Print)2162-7541
ISSN (Electronic)2162-755X

Other

Other12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017
CountryChina
CityGuiyang
Period10/25/1710/28/17

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

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