Automatic Test Program Generation for Pipelined Processors

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "Automatic Test Program Generation for Pipelined
  author =       "F. Corno and G. Cumani and M. {Sonza Reorda} and 
                 G. Squillero",
  publisher =    "ACM",
  year =         "2003",
  bibsource =    "DBLP,",
  booktitle =    "Proceedings of the 2003 ACM Symposium on Applied
                 Computing (SAC)",
  address =      "Melbourne, FL, USA",
  month =        "9-12 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  citeseer-isreferencedby = "oai:CiteSeerPSU:219188;
                 oai:CiteSeerPSU:183962; oai:CiteSeerPSU:139723;
  citeseer-references = "oai:CiteSeerPSU:472349; oai:CiteSeerPSU:276822;
                 oai:CiteSeerPSU:303540; oai:CiteSeerPSU:212034;
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:573140",
  rights =       "unrestricted",
  URL =          "",
  URL =          "",
  abstract =     "The continuous advances in micro-electronics design
                 are creating a significant challenge to design
                 validation in general, but tackling pipelined
                 microprocessors is remarkably more demanding. This
                 paper presents a methodology to automatically induce a
                 test program for a microprocessor maximising a given
                 verification metric. The approach exploits a new
                 evolutionary algorithm, close to Genetic Programming,
                 able to cultivate effective assembly language programs.
                 The proposed methodology was used to verify the
                 DLX/pII, an open-source processor with a 5-stage
                 pipeline. Code-coverage was adopted in the paper, since
                 it can be considered the required starting point for
                 any simulation-based functional verification processes.
                 Experimental results clearly show the effectiveness of
                 the approach.",

Genetic Programming entries for Fulvio Corno Gianluca Cumani Matteo Sonza Reorda Giovanni Squillero