Abstract | ||
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An effective clock-skew scheduling scheme in the high-level synthesis process targeted for power and speed optimization is presented. The proposed scheme has the following distinctive features: 1) a clock-skew management algorithm that selects a minimum set of clock phases to achieve the optimization goals is developed; 2) the effect of module binding in high-level synthesis was not considered in previous studies, which may lead to designs with timing violation; a discussion on how to model the effect of module binding is provided; 3) a heuristic low-power module binding algorithm that provides near-optimal results quickly is proposed; and 4) a technique called reallocation is proposed to exploit all available skews and thus maximize the capability of clock-skew scheduling. Experimental results show that, on the average, 48% power reduction is achieved by the proposed method. At most five clock phases are required, while in most cases two to four clock phases are sufficient. |
Year | DOI | Venue |
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2012 | 10.1109/TVLSI.2010.2091292 | IEEE Transactions on Very Large Scale Integration (VLSI) Systems |
Keywords | Field | DocType |
clock phase,speed optimization,power optimization,power aware computing,scheduling scheme,power aware high level synthesis,clock-skew scheduling,circuit optimisation,effective clock-skew scheduling scheme,optimization goal,power-aware high-level synthesis,clocks,low-power design,clock skew management,proposed scheme,low-power module binding algorithm,clock-skew management algorithm,high-level synthesis process,high-level synthesis,high level synthesis,heuristic algorithm,registers,clock skew,adders | Timing failure,Heuristic,Adder,Computer science,Scheduling (computing),Parallel computing,High-level synthesis,Real-time computing,Electronic engineering,Exploit,Clock skew,Power demand | Journal |
Volume | Issue | ISSN |
20 | 1 | 1063-8210 |
Citations | PageRank | References |
1 | 0.35 | 6 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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Tung-Hua Yeh | 1 | 5 | 1.79 |
Sying-Jyan Wang | 2 | 306 | 42.06 |