The incessant progress in manufacturing technology is posing new challenges to microprocessor designers. Nowadays, comprehensive verification of a chip can only be performed after tape-out, when the first silicon prototypes are available. Several activities that were originally supposed to be part of the pre-silicon design phase are migrating to this post-silicon time as well. The short paper describes a post-silicon methodology that can be exploited to devise functional failing tests. Such tests are essential to analyze and debug speed paths during verification, speed-stepping, and other critical activities. The proposed methodology is based on the Genetic Programming paradigm, and exploits a versatile toolkit named μGP. The paper demonstrates that an evolutionary algorithm can successfully tackle a significant and still open industrial problem. Moreover, it shows how to take into account complex hardware characteristics and architectural details of such complex devices.

Evolutionary failing-test generation for modern microprocessors / SANCHEZ SANCHEZ, EDGAR ERNESTO; Squillero, Giovanni; Tonda, ALBERTO PAOLO. - ELETTRONICO. - (2011). ((Intervento presentato al convegno GECCO'11 [10.1145/2001858.2001985].

Evolutionary failing-test generation for modern microprocessors

SANCHEZ SANCHEZ, EDGAR ERNESTO;SQUILLERO, Giovanni;TONDA, ALBERTO PAOLO
2011

Abstract

The incessant progress in manufacturing technology is posing new challenges to microprocessor designers. Nowadays, comprehensive verification of a chip can only be performed after tape-out, when the first silicon prototypes are available. Several activities that were originally supposed to be part of the pre-silicon design phase are migrating to this post-silicon time as well. The short paper describes a post-silicon methodology that can be exploited to devise functional failing tests. Such tests are essential to analyze and debug speed paths during verification, speed-stepping, and other critical activities. The proposed methodology is based on the Genetic Programming paradigm, and exploits a versatile toolkit named μGP. The paper demonstrates that an evolutionary algorithm can successfully tackle a significant and still open industrial problem. Moreover, it shows how to take into account complex hardware characteristics and architectural details of such complex devices.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2464584
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