Name
Abstract | ||
|---|---|---|
We resent a new al orithm RENO (Resynthesis for { i! Networ Optimization) or the optimization of multi-level combinational networks. In RENO, a given network is minimized for area by optimally resynthesizing each gate, using other existing gates in the network. The resynthesis process is based on a covering-set algorithm, which enables us to resynthesize usin complex gates instead of k only simple gates (e.g., NA D and NOR), thereby exploring more reconfiguration ossibilities. Due to the l? reconfiguration ability of the R NO algorithm, networks optimized by RENO have good ~ality even if no network don’t-care is used. The RE O algorithm has been implemented in both cube and shared-OBDD data strttcttrres. Experimental results obtained by RENO for benchmark functions and comparison with the optimization algorithm used in MIS 2.2 show that RENO is very effective for multi-level network optimization. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1145/127601.127712 | DAC |
Keywords | Field | DocType |
resynthesis approach,network optimization,boolean functions,data structures,distributed computing,network synthesis,observability,machinery | Boolean function,Boolean network,Permission,Data structure,Observability,Computer science,Network synthesis filters,Real-time computing,Control reconfiguration,Cube | Conference |
ISBN | Citations | PageRank |
0-89791-395-7 | 6 | 2.63 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuang-chien Chen | 1 | 347 | 30.84 |
| Yusuke Matsunaga | 2 | 362 | 57.26 |
| S. Muroga | 3 | 430 | 252.48 |
| Masahiro Fujita | 4 | 6 | 2.63 |