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Digital system simulation: methodologies and examples
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 35th annual Design Automation Conference table of contents
San Francisco, California, United States
Pages: 658 - 663  
Year of Publication: 1998
ISBN:0-89791-964-5
Authors
Kunle Olukotun  Computer Systems Laboratory, Stanford University, Stanford, CA
Mark Heinrich  Computer Systems Laboratory, Stanford University, Stanford, CA
David Ofelt  Computer Systems Laboratory, Stanford University, Stanford, CA
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
EDAC : Electronic Design Automation Consortium
IEEE-CS : Computer Society
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 23,   Citation Count: 5
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ABSTRACT

Two major trends in the digital design industry are the increase insystem complexity and the increasing importance of short designtimes. The rise in design complexity is motivated by consumerdemand for higher performance products as well as increases inintegration density which allow more functionality to be placed ona single chip. A consequence of this rise in complexity is a significantincrease in the amount of simulation required to design digitalsystems. Simulation time typically scales as the square of theincrease in system complexity [4]. Short design times are importantbecause once a design has been conceived there is a limited timewindow in which to bring the system to market while its performanceis competitive.Simulation serves many purposes during the design cycle of a digitalsystem. In the early stages of design, high-level simulation isused for performance prediction and analysis. In the middle of thedesign cycle, simulation is used to develop the software algorithmsand refine the hardware. In the later stages of design, simulation isused make sure performance targets are reached and to verify thecorrectness of the hardware and software. The different simulationobjectives require varying levels of modeling detail. To keep designtime to a minimum, it is critical to structure the simulation environmentto make it possible to trade-off simulation performance formodel detail in a flexible manner that allows concurrent hardwareand software development.In this paper we describe the different simulation methodologies fordeveloping complex digital systems, and give examples of one suchsimulation environment. The rest of this paper is organized as follows.In Section 2 we describe and classify the various simulationmethodologies that are used in digital system design and describehow they are used in the various stages of the design cycle. In Section3 we provide examples of the methodologies. We describe asophisticated simulation environment used to develop a large ASICfor the Stanford FLASH multiprocessor.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Z. Barzilai, J. L. Carter, B. K. Rosen, and J. D. Rutledge, "HSS- A high speed simulator, IEEE Transactions on Computer-AidedDesign, vol. CAD-6, pp. 601-617, 1987.
 
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J. Buck, S. Ha, E. lee, and D. Messerchmitt, "Ptolemy: a framework for simulating and prototyping heterogeneous systems," International Journal of Computer Simulation, January, 1990.
 
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D.M. Lewis, "A hierarchical compiled code even-driven logic simulator," IEEE Transactions on Computer-Aided Design, vol. 10, pp. 726-737, 1991.
 
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M. D. Smith, "Tracing with Pixie," Stanford University, Computer Systems-Laboratory, Technical CSL-TR-91- 497, November 199 1.
 
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Collaborative Colleagues:
Kunle Olukotun: colleagues
Mark Heinrich: colleagues
David Ofelt: colleagues