Details
| Original language | English |
|---|---|
| Pages (from-to) | 520-530 |
| Number of pages | 11 |
| Journal | Journal of Systems Architecture |
| Volume | 61 |
| Issue number | 10 |
| Publication status | Published - 26 Jul 2015 |
| Externally published | Yes |
Abstract
Multiprocessor system-on-chip (MPSoC) designs offer a lot of computational power assembled in a compact design. In mobile robotic applications, they offer the chance to replace several dedicated computing boards by a single processor, which typically leads to a significant acceleration of the computer-vision algorithms employed. This enables robots to perform more complex tasks at lower power budgets, less cooling overhead and, ultimately, smaller physical dimensions. However, the presence of shared resources and dynamically varying load situations leads to low throughput and quality for corner detection; an algorithm very widely used in computer-vision. The contemporary operating systems from the domain have not been designed for the management of highly parallel but shared computing resources. In this paper, we evaluate resource-aware programming as a means to overcome these issues. Our work is based on Invasive Computing, a MPSoC hardware and operating-system design for resource-aware programming. We evaluate this system with real-world algorithms, like Harris and Shi-Tomasi corner detectors. Our results indicate that resource-aware programming can lead to significant improvements in the behavior of these detectors, with up to 22 percent improvement in throughput and up to 20 percent improvement in accuracy.
Keywords
- Computer vision, Corner detection, Invasive Computing, Resource-aware programming, Self-adaptive algorithms
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Hardware and Architecture
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In: Journal of Systems Architecture, Vol. 61, No. 10, 26.07.2015, p. 520-530.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Self-adaptive corner detection on MPSoC through resource-aware programming
AU - Paul, Johny
AU - Oechslein, Benjamin
AU - Erhardt, Christoph
AU - Schedel, Jens
AU - Kröhnert, Manfred
AU - Lohmann, Daniel
AU - Stechele, Walter
AU - Asfour, Tamim
AU - Schröder-Preikschat, Wolfgang
PY - 2015/7/26
Y1 - 2015/7/26
N2 - Multiprocessor system-on-chip (MPSoC) designs offer a lot of computational power assembled in a compact design. In mobile robotic applications, they offer the chance to replace several dedicated computing boards by a single processor, which typically leads to a significant acceleration of the computer-vision algorithms employed. This enables robots to perform more complex tasks at lower power budgets, less cooling overhead and, ultimately, smaller physical dimensions. However, the presence of shared resources and dynamically varying load situations leads to low throughput and quality for corner detection; an algorithm very widely used in computer-vision. The contemporary operating systems from the domain have not been designed for the management of highly parallel but shared computing resources. In this paper, we evaluate resource-aware programming as a means to overcome these issues. Our work is based on Invasive Computing, a MPSoC hardware and operating-system design for resource-aware programming. We evaluate this system with real-world algorithms, like Harris and Shi-Tomasi corner detectors. Our results indicate that resource-aware programming can lead to significant improvements in the behavior of these detectors, with up to 22 percent improvement in throughput and up to 20 percent improvement in accuracy.
AB - Multiprocessor system-on-chip (MPSoC) designs offer a lot of computational power assembled in a compact design. In mobile robotic applications, they offer the chance to replace several dedicated computing boards by a single processor, which typically leads to a significant acceleration of the computer-vision algorithms employed. This enables robots to perform more complex tasks at lower power budgets, less cooling overhead and, ultimately, smaller physical dimensions. However, the presence of shared resources and dynamically varying load situations leads to low throughput and quality for corner detection; an algorithm very widely used in computer-vision. The contemporary operating systems from the domain have not been designed for the management of highly parallel but shared computing resources. In this paper, we evaluate resource-aware programming as a means to overcome these issues. Our work is based on Invasive Computing, a MPSoC hardware and operating-system design for resource-aware programming. We evaluate this system with real-world algorithms, like Harris and Shi-Tomasi corner detectors. Our results indicate that resource-aware programming can lead to significant improvements in the behavior of these detectors, with up to 22 percent improvement in throughput and up to 20 percent improvement in accuracy.
KW - Computer vision
KW - Corner detection
KW - Invasive Computing
KW - Resource-aware programming
KW - Self-adaptive algorithms
UR - http://www.scopus.com/inward/record.url?scp=84948581449&partnerID=8YFLogxK
U2 - 10.1016/j.sysarc.2015.07.011
DO - 10.1016/j.sysarc.2015.07.011
M3 - Article
AN - SCOPUS:84948581449
VL - 61
SP - 520
EP - 530
JO - Journal of Systems Architecture
JF - Journal of Systems Architecture
SN - 1383-7621
IS - 10
ER -