Abstract

High-level synthesis is a technology that converts a software program into hardware. High-level synthesis can greatly reduce the load of hardware design and development. An FPGA is a device that can implement any digital hardware at any time. The combination of high-level synthesis and FPGA has been attracting attention in the field of embedded devices where the life cycle of products is short and the demand for power-saving and high-performance products is high. High-level synthesis cannot convert pure software that describes the algorithm intuitively into high-performance and low-power hardware. High-level synthesis requires an input program that considers the hardware configuration in order to generate good hardware. In this paper, we develop hardware for histogram equalization to improve image contrast using high-level synthesis. In the description of the program, in addition to general methods such as fixed-point conversion, the description method we have developed is applied. Experimental results show that the histogram equalization hardware can achieve 2.3 times performance improvement compared to software implementation.

Highlights

  • High-level synthesis is a technology that converts a software program into hardware

  • High-level synthesis can greatly reduce the load of hardware design and development

  • An FPGA is a device that can implement any digital hardware at any time

Read more

Summary

Introduction

High-level synthesis is a technology that converts a software program into hardware. High-level synthesis can greatly reduce the load of hardware design and development. An FPGA is a device that can implement any digital hardware at any time. The combination of high-level synthesis and FPGA has been attracting attention in the field of embedded devices where the life cycle of products

Flow Chart
Luminance histogram generation
NCHG generation
Luminance transformation
Pure histogram equalization
Fixed point formatting
Port duplication to same argument
Experiment and Discussion
Conclusions
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.