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Soft error optimization of standard cell circuits based on gate sizing and multi-objective genetic algorithm

  • Sheng Weiguang*
  • , Liyi Xiao
  • , Zhigang Mao
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A radiation harden technique based on gate sizing and multi-objective genetic algorithm (MOGA) is developed to optimize the soft error tolerance of standard cell circuits. Soft error rate (SER), chip area and longest path delay are selected as the optimization goals and fast fitness evaluation algorithms for the three goals are developed and embedded into the MOGA. All the three goals are optimized simultaneously by optimally sizing the gates in the circuit, which is a complex NP-Complete problem and resolved by MOGA through exploring the global design space of the circuit. Syntax analysis technique is also employed to make the proposed framework can optimize not only pure combinational logic circuit but also the combinational parts of sequential logic circuit.Optimizing experiments carried out on ISCAS'85 and ISCAS'89 standard benchmark circuits show that the proposed optimization algorithm can decrease the SER 74.25% with very limited delay overhead (0.28%). Furthermore, the algorithm can also reduce the area for most of the circuit under test by average 5.23%. The proposed technique is proved to be better than other works in delay and area overhead and suitable to direct the design of soft error tolerance integrated circuits in high reliability realms.

Original languageEnglish
Title of host publication2009 46th ACM/IEEE Design Automation Conference, DAC 2009
Pages502-507
Number of pages6
StatePublished - 2009
Event2009 46th ACM/IEEE Design Automation Conference, DAC 2009 - San Francisco, CA, United States
Duration: 26 Jul 200931 Jul 2009

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference2009 46th ACM/IEEE Design Automation Conference, DAC 2009
Country/TerritoryUnited States
CitySan Francisco, CA
Period26/07/0931/07/09

Keywords

  • Genetic algorithm
  • Multi-objective
  • Optimization
  • Soft error

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