Name
Abstract | ||
|---|---|---|
We propose a non-sequential wire routing method, LMR (Lagrangian method for wire routing) and a neural network model called LPH (Lagrange programming neural network with high order connection). The wire routing problem is formalized as a continuous valued constrained optimization problem and LMR is based on the Lagrangian method for the constrained optimization problem. In LMR, all the nets are routed simultaneously, which means that the problem of deciding the ordering of the nets doesn't exist. The rip-up reroute for all the nets occur simultaneously by some mutual competition of the nets. These features of LMR improve the completion rate and makes it suitable for massively parallel computing i.e. neurocomputing. The proposed neural network model LPH a's well-suited for LMR. |
Year | Venue | Keywords |
|---|---|---|
1998 | ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3 | neural network |
Field | DocType | Citations |
Computer science,Stochastic neural network,Recurrent neural network,Time delay neural network,Routing (electronic design automation),Artificial intelligence,Artificial neural network,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shakeel Ismail | 1 | 0 | 0.34 |
| Masahiro Nagamatu | 2 | 5 | 4.64 |
| T. Yanaru | 3 | 16 | 6.03 |