Name
Abstract | ||
|---|---|---|
SC (stochastic computation) has been found to be very advantageous in image processing applications because of its lower area consumption and low-power operation. However, one of the major issues with the SC is its long run-time requirement for accurate results. In this paper, a new technique called the approximate stochastic computing (ASC) approach called the approximate stochastic computing (ASC) focusing on image processing applications is proposed to reduce the computation time of a SC by a factor of 16 at a trade-off of an error percentage of 3.13% in the absolute stochastic value ([0,1)) computed. The proposed technique considers only the first four MSBs of the image pixel value for SC, which introduce a maximum error of 6.25% in the stochastic output. Attempts have been made to reduce this error to 3.13% by linearly increasing the clock cycles from 16 to 17 rather than exponentially (ex: 32, 64,128,256...). Experimental results from SC edge detection circuit indicate that this technique is a promising approach for efficient approximate image processing. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ISOCC.2016.7799758 | 2016 International SoC Design Conference (ISOCC) |
Keywords | Field | DocType |
SC edge detection circuit,clock cycles,stochastic output,maximum error,image pixel value,MSB,absolute stochastic value,computation time reduction,ASC approach,image processing applications,approximate stochastic computing | Computer science,Edge detection,Stochastic process,Image processing,Algorithm,Electronic engineering,Pixel,Stochastic computing,Grayscale,Computation,Exponential growth | Conference |
ISSN | ISBN | Citations |
2163-9612 | 978-1-5090-3220-4 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramu Seva | 1 | 0 | 1.69 |
| Prashanthi Metku | 2 | 0 | 2.70 |
| Kyung Ki Kim | 3 | 99 | 21.62 |
| Yong-Bin Kim | 4 | 22 | 7.14 |
| Minsu Choi | 5 | 156 | 27.63 |