DocumentCode :
2589616
Title :
Generic pipelined processor modeling and high performance cycle-accurate simulator generation
Author :
Reshadi, Mehrdad ; Dutt, Nikil
Author_Institution :
Donald Bren Sch. of Inf. & Comput. Sci., California Univ., Irvine, CA, USA
fYear :
2005
fDate :
7-11 March 2005
Firstpage :
786
Abstract :
Detailed modeling of processors and high performance cycle-accurate simulators are essential for today´s hardware and software design. These problems are challenging enough by themselves and have seen many previous research efforts. Addressing both simultaneously is even more challenging, with many existing approaches focusing on one over another. In this paper, we propose the reduced colored Petri net (RCPN) model that has two advantages: first, it offers a very simple and intuitive way of modeling pipelined processors: second, it can generate high performance cycle-accurate simulators. RCPN benefits from all the useful features of colored Petri nets without suffering from their exponential growth in complexity. RCPN processor models are very intuitive since they are a mirror image of the processor pipeline block diagram. Furthermore, in our experiments on the generated cycle-accurate simulators for XScale and StrongArm processor models, we achieved an order of magnitude (∼15 times) speedup over the popular SimpleScalar ARM simulator.
Keywords :
Petri nets; circuit simulation; hardware-software codesign; microprocessor chips; performance evaluation; pipeline processing; RCPN model; StrongArm; XScale; cycle-accurate simulator generation; generic pipelined processor modeling; hardware software design; performance; reduced colored Petri net; Computational modeling; Computer simulation; Embedded computing; Hardware; Hazards; High performance computing; Mirrors; Pipelines; Resource management; Software design;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Design, Automation and Test in Europe, 2005. Proceedings
ISSN :
1530-1591
Print_ISBN :
0-7695-2288-2
Type :
conf
DOI :
10.1109/DATE.2005.166
Filename :
1395674
Link To Document :
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