Name
Abstract | ||
|---|---|---|
The construction of histograms is an integral part of image processing pipelines, useful for image editing features such as histogram matching, thresholding and histogram equalization. In the past, research done on kernels used in image processing pipelines target advancements to achieve high throughput, area efficiency and low cost. However, a growing topic of interest that has not been fully explored is the use of energy efficiency as a key metric. In this work, we focus on developing an energy-efficient histogram implementation with a minimum frame rate of at least 30 frames per second. We determine the components that consume the most power and propose an optimized histogram implementation with the utilization of multiple optimizations to achieve notable improvement in energy efficiency while maintaining suitable throughput for usage within image processing pipelines. These optimizations include a data-defined memory activation schedule, a careful data layout and circuit-level pipelining. Our architecture is implemented on commonly-used image sizes which vary from 240 × l28 to 1216×912 and assume a pixel width of 16 bits per pixel. The post place-and-route results show that our optimized architecture has up to 15.3× higher energy efficiency when compared against the baseline architecture. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ReConFig.2014.7032517 | ReConFigurable Computing and FPGAs |
Keywords | Field | DocType |
energy conservation,field programmable gate arrays,image processing,pipeline processing,power aware computing,FPGA,architecture optimization,baseline architecture,circuit-level pipelining,data layout,data-defined memory activation schedule,energy efficiency,energy-efficient histogram,frame rate,histogram equalization,histogram matching,image editing features,image processing pipeline,image size,optimized histogram implementation,pixel width,power consumption,thresholding,FPGA,energy efficiency,histogram,memory activation scheduling | Histogram,Computer science,Parallel computing,Histogram matching,Image processing,Real-time computing,Adaptive histogram equalization,Color depth,Pixel,Thresholding,Histogram equalization | Conference |
ISSN | Citations | PageRank |
2325-6532 | 2 | 0.40 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Sanny | 1 | 16 | 2.22 |
| Yi-Hua E. Yang | 2 | 151 | 11.14 |
| Viktor K. Prasanna | 3 | 7211 | 762.74 |