A technique is proposed to reduce the peak power consumption of sequential circuits during test pattern application. High-speed computation intensive VLSI systems, as telecommunication systems, make power management during test a critical problem. A Genetic Algorithm computes a set of redundant test sequences, then a genetic optimization algorithm selects the optimal subset of sequences able to reduce the consumed power, without reducing the fault coverage. Experimental results gathered on benchmark circuits show that our approach decreases the peak power consumption by 20% on the average with respect to the original test sequence generated ignoring the power dissipation problem, without affecting the fault coverage.

Test Pattern Generation under Low Power Constraints / Corno, Fulvio; Rebaudengo, Maurizio; SONZA REORDA, Matteo; Violante, Massimo. - STAMPA. - 1596:(1999), pp. 162-170. (Intervento presentato al convegno 1st European Workshops, EvoIASP’99 and EuroEcTel’99 tenutosi a Göteborg (SWE) nel May 26-27, 1999) [10.1007/10704703_13].

Test Pattern Generation under Low Power Constraints

CORNO, Fulvio;REBAUDENGO, Maurizio;SONZA REORDA, Matteo;VIOLANTE, MASSIMO
1999

Abstract

A technique is proposed to reduce the peak power consumption of sequential circuits during test pattern application. High-speed computation intensive VLSI systems, as telecommunication systems, make power management during test a critical problem. A Genetic Algorithm computes a set of redundant test sequences, then a genetic optimization algorithm selects the optimal subset of sequences able to reduce the consumed power, without reducing the fault coverage. Experimental results gathered on benchmark circuits show that our approach decreases the peak power consumption by 20% on the average with respect to the original test sequence generated ignoring the power dissipation problem, without affecting the fault coverage.
1999
3540658378
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2374690
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