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Extracting a Simplified View of Design Functionality Based on Vector Simulation

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Hardware and Software, Verification and Testing (HVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 4383))

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Abstract

This paper presents a simulation-based methodology for extracting a simplified view of design functionality from a given module. Such a simplified design view can be used to facilitate test pattern justification from the outputs of the module to the inputs of the module. In this work, we formulate this type of design simplification as a learning problem. By developing a scheme for learning word-level functions, we point out that the core of the problem is to develop an efficient Boolean learner. We discuss the implementation of such a Boolean learner and compare its performance with the one of best-known learning algorithms, the Fourier analysis based method. Experimental results are presented to illustrate the implementation of the simulation-based methodology and its usage for extracting a simplified view of Open RISC 1200 datapath.

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References

  1. Aharon, A., et al.: Test Program Generation for Functional Verification of PowerPC Processors in IBM. In: Proc. DAC, pp. 279–285 (1995)

    Google Scholar 

  2. Chen, L., et al.: A Scalable Sofware-Based Self-Test Methodology for Programmable Processors. In: Proc. Design Automation Conf., Anaheim, pp. 548–553 (2003)

    Google Scholar 

  3. Wen, C., et al.: On A Software-Based Self-Test Methodology and Its Application. In: Proc. VLSI Test Symp., Palm Springs (2005)

    Google Scholar 

  4. Wen, C., Wang, L., Chang, K.-T.: Simulation-Based Functional Test Generation for Embedded Processors. To be accepted in IEEE Transaction on Computer (2006)

    Google Scholar 

  5. Bryant, R.E.: Symbolic Boolean Manipulation with Ordered Binary- Decision Diagrams. ACM Computing Surveys 24(3), 293–318 (1992)

    Article  Google Scholar 

  6. Valiant, L.: A theory of the learnable. Communications of the ACM 27(11), 1134–1142 (1984)

    Article  MATH  Google Scholar 

  7. Kearns, M.J., Vazirani, U.V.: An Introduction to Computational Learning Theory. MIT Press, Cambridge (1994)

    Google Scholar 

  8. Kearns, M., et al.: On the learnability of Boolean formulae. In: Proc. 19th Symp. on Theory of Computing, pp. 285–295 (1987)

    Google Scholar 

  9. Kearns, M., Valiant, L.: Learning Boolean formulae or finite automata is as hard as factoring. Technical Report TR-14-88, Harvard University (1988)

    Google Scholar 

  10. Linial, N., Mansour, Y., Nisan, N.: Constant depth circuits, Fourier transform, and learnability. J. of ACM 40(3), 607–620 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  11. Mansour, Y.: Implementation Issues in the Fourier Transform Algorithm. Machine Learning 40, 5–33 (2000)

    Article  MATH  Google Scholar 

  12. Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991)

    Article  Google Scholar 

  13. Ben-Or, M., Tiwari, P.: A deterministic algorithm for sparse multivariate polynomial interpolation. In: Proc. 12th ACM Symp. Theory Comput., pp. 301–309. ACM Press, New York (1988)

    Google Scholar 

  14. Zippel, R.: Interpolating polynomials from their values. J. Symb. Comput. 9(3), 375–403 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  15. Phillips, G.M.: Interpolation and Approximation by Polynomials. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  16. Schwartz, J.: Fast probabilistic algorithms for verification of polynomial identities. Jour. ACM 27(4), 701–717 (1980)

    Article  MATH  Google Scholar 

  17. OpenRISC 1200 at http://www.opencores.org/

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Eyal Bin Avi Ziv Shmuel Ur

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© 2007 Springer Berlin Heidelberg

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Guzey, O., Wen, C., Wang, LC., Feng, T., Miller, H., Abadir, M.S. (2007). Extracting a Simplified View of Design Functionality Based on Vector Simulation. In: Bin, E., Ziv, A., Ur, S. (eds) Hardware and Software, Verification and Testing. HVC 2006. Lecture Notes in Computer Science, vol 4383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70889-6_3

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  • DOI: https://doi.org/10.1007/978-3-540-70889-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70888-9

  • Online ISBN: 978-3-540-70889-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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