Stochastic Edge Detection for Fine-Grained Progressive Precision

被引：1

作者：

Lee, Youngwook ^{[1
]}

Kim, Kyung-Ki ^{[2
]}

Kim, Yong-Bin ^{[3
]}

Choi, Minsu ^{[4
]}

机构：

[1] Keimyung Univ, Dept Mech Engn, Daegu, South Korea

[2] Daegu Univ, Dept Elctron Engn, Gyongsan, South Korea

[3] Northeastern Univ, Dept ECE, Boston, MA 02115 USA

[4] Missouri Univ Sci & Technol, Dept ECE, Rolla, MO 65409 USA

来源：

18TH INTERNATIONAL SOC DESIGN CONFERENCE 2021 (ISOCC 2021) | 2021年

关键词：

Stochastic computing; low discrepancy sequence; edge detectors; progressive precision;

D O I：

10.1109/ISOCC53507.2021.9614036

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Stochastic Computing (SC) is a method of performing an operation by expressing a probability in a bitstream. This format is simpler than the format of conventional binary computing and can be implemented in hardware with fewer resources. In addition, by using a Low-Discrepancy (LD) sequence, faster convergence can be derived. This paper shows the experimental results of applying SC using LD sequence to edge detection algorithms including Sobel and Roberts Cross to analyze the progressive precision performance and scalability.

引用

页码：119 / 120

页数：2

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