Suave: Object-Oriented and Genericity Extensions to VHDL for High-Level Modeling

Author(s):  
Peter J. Ashenden ◽  
Philip A. Wilsey ◽  
Dale E. Martin
2014 ◽  
Vol 599-601 ◽  
pp. 530-533
Author(s):  
Hong Hao Wang ◽  
Hui Quan Wang ◽  
Zhong He Jin

Due to the complex timing sequence of NAND flash, a unified design process is urgently required to guarantee the reliability of storage system of nano-satellite. Unified Modeling Language (UML) is a widely used high level modeling language for object-oriented design. This paper adopts the UML as the design and modelling tool in the low level storage system design to elaborate the UML application in each phase of design in detail. The result shows taking UML as the modelling tool results in a clear and unambiguity design, which promotes the reliability and quality of software. At last, the feasibility of object-oriented implementation in C is presented.


VLSI Design ◽  
1999 ◽  
Vol 10 (2) ◽  
pp. 217-235 ◽  
Author(s):  
Peter J. Ashenden ◽  
Philip A. Wilsey

This paper reviews proposals for extensions to VHDL to support high-level modeling and places them within a taxonomy that describes the modeling requirements they address. Many of the proposals focus on object-oriented extensions, whereas this paper argues that extension of VHDL to support high-level modeling requires a broader review. The paper presents a detailed discussion of issues to be considered in adding high-level modeling extensions to VHDL, including concurrency and communication, abstraction using entity interfaces, object-oriented data modeling, encapsulation, signal assignment semantics, shared variables, multiple inheritance, genericity and synthesis. Emphasis is placed on the importance of designing simple orthogonal semantic mechanisms that interact in well defined ways, and that integrate cleanly with existing language features.


Author(s):  
Michael M. Tiller ◽  
Jonathan A. Dantzig

Abstract In this paper we discuss the design of an object-oriented framework for simulation and optimization. Although oriented around high-level problem solving, the framework defines several classes of problems and includes concrete implementations of common algorithms for solving these problems. Simulations are run by combining these algorithms, as needed, for a particular problem. Included in this framework is the capability to compute the sensitivity of simulation results to the different simulation parameters (e.g. material properties, boundary conditions, etc). This sensitivity information is valuable in performing optimization because it allows the use of gradient-based optimization algorithms. Also included in the system are many useful abstractions and implementations related to the finite element method.


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