Name
Abstract | ||
|---|---|---|
Vertex-centric graph computations are widely used in many machine learning and data mining applications that operate on graph data structures. This paper presents GraphGen, a vertex-centric framework that targets FPGA for hardware acceleration of graph computations. GraphGen accepts a vertex-centric graph specification and automatically compiles it onto an application-specific synthesized graph processor and memory system for the target FPGA platform. We report design case studies using GraphGen to implement stereo matching and handwriting recognition graph applications on Terasic DE4 and Xilinx ML605 FPGA boards. Results show up to 14.6x and 2.9x speedups over software on Intel Core i7 CPU for the two applications, respectively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FCCM.2014.15 | FCCM |
Keywords | Field | DocType |
learning artificial intelligence,graph theory,data mining,field programmable gate arrays,hardware,formal specification,data structures,integrated circuit design,hardware acceleration,handwriting recognition | Data structure,Graph database,Vertex (geometry),Computer science,Parallel computing,Handwriting recognition,Field-programmable gate array,Software,Hardware acceleration,Computation | Conference |
Citations | PageRank | References |
42 | 1.48 | 11 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eriko Nurvitadhi | 1 | 399 | 33.08 |
| Gabriel Weisz | 2 | 144 | 8.30 |
| Yu Wang | 3 | 57 | 2.44 |
| Skand Hurkat | 4 | 49 | 2.32 |
| Marie Nguyen | 5 | 42 | 2.83 |
| James C. Hoe | 6 | 2048 | 141.34 |
| José-jesús Fernández | 7 | 1584 | 111.72 |
| Carlos Guestrin | 8 | 9220 | 488.92 |