Abstract
This paper proposes a group-based very fast simulated annealing (GB-VFSA) algorithm to achieve higher-quality placement in a shorter CPU time. We first introduce a temperature-dependent perturbation model based on Cauchy distribution to generate new solutions. Then, we put high-connected blocks into one group and use a group as a unit for placement. In order to avoid premature convergence of the algorithm, multiple potential solutions are used to search the solution space at the same time. The idea "pheromone" which comes from ant colony optimization is used to realize the communication between multiple potential solutions. Experimental results using MCNC beachmarks show that GB-VFSA achieved 23% reduction in CPU time and 3.6% improvement in maximal time delay over traditional simulating annealing algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 542-548 |
| Number of pages | 7 |
| Journal | International Journal of Machine Learning and Computing |
| Volume | 8 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2018 Dec 1 |
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Keywords
- Block group
- Island style FPGAs
- Placement
- Potential solution group
- Simulating annealing
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence
Cite this
Efficient simulated annealing-based placement algorithm for Island style FPGAs. / Shi, Runxiao; Ma, Lan; Watanabe, Takahiro.
In: International Journal of Machine Learning and Computing, Vol. 8, No. 6, 01.12.2018, p. 542-548.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Efficient simulated annealing-based placement algorithm for Island style FPGAs
AU - Shi, Runxiao
AU - Ma, Lan
AU - Watanabe, Takahiro
PY - 2018/12/1
Y1 - 2018/12/1
N2 - This paper proposes a group-based very fast simulated annealing (GB-VFSA) algorithm to achieve higher-quality placement in a shorter CPU time. We first introduce a temperature-dependent perturbation model based on Cauchy distribution to generate new solutions. Then, we put high-connected blocks into one group and use a group as a unit for placement. In order to avoid premature convergence of the algorithm, multiple potential solutions are used to search the solution space at the same time. The idea "pheromone" which comes from ant colony optimization is used to realize the communication between multiple potential solutions. Experimental results using MCNC beachmarks show that GB-VFSA achieved 23% reduction in CPU time and 3.6% improvement in maximal time delay over traditional simulating annealing algorithm.
AB - This paper proposes a group-based very fast simulated annealing (GB-VFSA) algorithm to achieve higher-quality placement in a shorter CPU time. We first introduce a temperature-dependent perturbation model based on Cauchy distribution to generate new solutions. Then, we put high-connected blocks into one group and use a group as a unit for placement. In order to avoid premature convergence of the algorithm, multiple potential solutions are used to search the solution space at the same time. The idea "pheromone" which comes from ant colony optimization is used to realize the communication between multiple potential solutions. Experimental results using MCNC beachmarks show that GB-VFSA achieved 23% reduction in CPU time and 3.6% improvement in maximal time delay over traditional simulating annealing algorithm.
KW - Block group
KW - Island style FPGAs
KW - Placement
KW - Potential solution group
KW - Simulating annealing
UR - http://www.scopus.com/inward/record.url?scp=85057387660&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057387660&partnerID=8YFLogxK
U2 - 10.18178/ijmlc.2018.8.6.743
DO - 10.18178/ijmlc.2018.8.6.743
M3 - Article
AN - SCOPUS:85057387660
VL - 8
SP - 542
EP - 548
JO - International Journal of Machine Learning and Computing
JF - International Journal of Machine Learning and Computing
SN - 2010-3700
IS - 6
ER -