Domain-Specific Automatic Programming
IEEE Transactions on Software Engineering - Special issue on artificial intelligence and software engineering
Behavioral models take the pain out of system simulation
Computer Design
COSMOS: a compiled simulator for MOS circuits
DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
Automatic generation of behavioral models from switch-level descriptions
DAC '89 Proceedings of the 26th ACM/IEEE Design Automation Conference
New design error modeling and metrics for design validation
EURO-DAC '92 Proceedings of the conference on European design automation
Modeling and Simulation of Design Errors
ICCD '92 Proceedings of the 1991 IEEE International Conference on Computer Design on VLSI in Computer & Processors
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The simulation automation system (SAS) was developed to provide an efficient simulation environment, by automating the entire simulation process. This system can be classified, by its salient unique features, into: automatic model generator (AMG), automatic simulator developer (ASD), and design error simulation and test system (DEST). The system can automatically generate multivalued simulation models and automatically develop various simulators, using domain specific automatic programming techniques. The automatic model generation feature can be used when a new model library is built or when an existing library is upgraded. The automatic simulator development feature allows a user who may not be knowledgeable about simulators, to easily develop unique simulators, which can be used for special purposes or special designs. SAS can also verify designs using the Design Error Simulation and Test System. It provides a confidence measure of the verification, as well as simulation results. When users are not satisfied with the confidence level achieved after simulation, they can automatically generate additional simulation patterns for design errors in order to achieve a higher confidence level. Using this approach, design verification time and cost can be considerably reduced, and an actual measure of verification is provided. Consequently, the design cycle can be considerably reduced. This is especially significant for large, complex systems.