Programming Systems for Parallelizing VLSI CAD and Beyond

2020 ◽  
Author(s):  
Tsung-Wei Huang
Keyword(s):  
Vlsi Cad ◽  
Programming Systems

ILPNET repositories on WWW: Inductive Logic Programming systems, datasets and bibliography

1996 ◽  
Vol 9 (4) ◽  
pp. 157-206 ◽  
Author(s):  
Nada Lavrač ◽  
Irene Weber ◽  
Darko Zupanič ◽  
Dimitar Kazakov ◽  
Olga Štěpánková ◽  
...  
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Programming Systems

A VLSI — CAD system for efficient design of CMOS/390 processors

1991 ◽  
Vol 32 (1-5) ◽  
pp. 227-234
Author(s):  
A. Tietz ◽  
J. Koehl
Keyword(s):  
Efficient Design ◽  
Vlsi Cad ◽  
Cad System

scholarly journals Programming Systems for use in Research Computation

1966 ◽  
Vol 8 (4) ◽  
pp. 315-318 ◽  
Author(s):  
J. M. Craddock
Keyword(s):  
Programming Systems

scholarly journals Enhancing disjunctive logic programming systems by SAT checkers

2003 ◽  
Vol 151 (1-2) ◽  
pp. 177-212 ◽  
Author(s):  
Christoph Koch ◽  
Nicola Leone ◽  
Gerald Pfeifer
Keyword(s):  
Logic Programming ◽  
Programming Systems

CAD-Systeme und NC-Programmiersysteme koppeln / Interlinking CAD systems and NC programming systems

1990 ◽  
Vol 85 (5) ◽  
pp. 267-271
Author(s):  
W. Eversheim ◽  
B. Dahl ◽  
G. Marczinski ◽  
M. Holland
Keyword(s):  
Cad Systems ◽  
Nc Programming ◽  
Programming Systems

Machine Learning for VLSI CAD: A Case Study in On-Chip Power Grid Design

2021 ◽  
Author(s):  
Sukanta Dey ◽  
Sukumar Nandi ◽  
Gaurav Trivedi
Keyword(s):  
Machine Learning ◽  
Power Grid ◽  
Vlsi Cad ◽  
Grid Design ◽  
On Chip

Programming Systems

1994 ◽  
pp. 56-94
Author(s):  
James S. Royer ◽  
John Case
Keyword(s):  
Programming Systems

scholarly journals Exploring genetic programming systems with MAP-Elites

2018 ◽  
Author(s):  
Emily Dolson ◽  
Alexander Lalejini ◽  
Charles Ofria
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Pareto Front ◽  
Underlying Structure ◽  
Phenotypic Traits ◽  
Computation Technique ◽  
Phenotype Space ◽  
Programming Systems ◽  
Insight Into ◽  
Connection Cost

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

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