Name
Abstract | ||
|---|---|---|
Proximity Query (PQ) is a process to calculate the relative placement of objects. It is a critical task for many applications such as robot motion planning, but it is often too computationally demanding for real-time applications, particularly those involving human-robot collaborative control. This paper derives a PQ formulation which can support non-convex objects represented by meshes or cloud points. We optimise the proposed PQ for reconfigurable hardware by function transformation and reduced precision, resulting in a novel data structure and memory architecture for data streaming while maintaining the accuracy of results. Run-time reconfiguration is adopted for dynamic precision optimisation. Experimental results show that our optimised PQ implementation on a reconfigurable platform with four FPGAs is 58 times faster than an optimised CPU implementation with 12 cores, 9 times faster than a GPU, and 3 times faster than a double precision implementation with four FPGAs. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FPT.2013.6718355 | PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY (FPT) |
Keywords | Field | DocType |
reconfigurable hardware,data structures,field programmable gate arrays,data structure,real time,mesh generation,cloud point | Data structure,Polygon mesh,Computer science,Parallel computing,Double-precision floating-point format,Field-programmable gate array,Real-time computing,Memory architecture,Control reconfiguration,Reconfigurable computing,Cloud computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas C. P. Chau | 1 | 53 | 6.81 |
| Ka Wai Kwok | 2 | 167 | 27.10 |
| Gary C. T. Chow | 3 | 46 | 4.39 |
| K. H. Tsoi | 4 | 399 | 38.79 |
| Kit-Hang Lee | 5 | 17 | 6.89 |
| Zion Tse | 6 | 0 | 0.34 |
| Peter Y. K. Cheung | 7 | 1720 | 208.45 |
| Wayne Luk | 8 | 3752 | 438.09 |