Name
Title | ||
|---|---|---|
A 470mV 2.7mW feature extraction-accelerator for micro-autonomous vehicle navigation in 28nm CMOS |
Abstract | ||
|---|---|---|
This paper proposes a power-efficient speeded-up robust features (SURF) extraction accelerator targeted primarily for micro air vehicles (MAVs) with autonomous navigation (Fig. 9.7.1). Typical object recognition SoCs [4-6] employ an application-specific algorithm to choose specific regions of interest (ROIs) to reduce computation by focusing on a small portion of the image. However, this approach is not feasible in applications where the whole image must be analyzed, such as visual navigation that requires the extraction of general features to determine location or movement. In addition, multicore architectures need to run at high clock frequencies to meet high peak performance requirements and the power consumption of inter-core communication becomes prohibitive. Since feature extraction algorithms require significant memory accesses across a large area, parallelization in a multicore system requires costly high-bandwidth memories for massive intermediate data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISSCC.2013.6487684 | ISSCC |
Keywords | Field | DocType |
space vehicle electronics,visual navigation,cmos integrated circuits,power-efficient speeded-up robust feature extraction accelerator,power consumption,size 28 nm,regions of interest,aircraft navigation,roi,system-on-chip,autonomous aerial vehicles,cmos technology,object recognition soc,feature extraction,application-specific algorithm,high-bandwidth memories,multicore architectures,surf extraction accelerator,power 2.7 mw,object recognition,intercore communication,voltage 470 mv,multicore system,microautonomous vehicle navigation,system on chip | Feature extraction algorithm,System on a chip,Computer science,Electronic engineering,Real-time computing,CMOS,Feature extraction,Multi-core processor,Electrical engineering,Computation,Power consumption,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | ISBN |
56 | 0193-6530 | 978-1-4673-4515-6 |
Citations | PageRank | References |
7 | 0.78 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongsuk Jeon | 1 | 183 | 21.01 |
| Yejoong Kim | 2 | 276 | 31.29 |
| Inhee Lee | 3 | 275 | 33.89 |
| Zhengya Zhang | 4 | 502 | 48.41 |
| David Blaauw | 5 | 8916 | 823.47 |
| Dennis Sylvester | 6 | 5295 | 535.53 |