DocumentCode
127089
Title
Accelerating accurate fault tree analysis using HW/SW co-design
Author
Cheshmikhani, Elham ; Zarandi, Hamid Reza ; Aliee, Hananeh
Author_Institution
Dept. of Comput. Eng. & Inf. Technol., Amirkabir Univ. of Technol., Tehran, Iran
fYear
2014
fDate
27-30 Jan. 2014
Firstpage
1
Lastpage
6
Abstract
Fault tree analysis is a widespread-use method for measuring dependability parameters such as reliability and safety. Fault tree analysis is more popular than Markov modeling and Reliability Block Diagram (RBD), which need good background of mathematical equations and impose complex analysis for large systems. In this paper, accurate analysis of the fault tree is accelerated using a hardware/ software (HW/SW) co-design. This method converts a fault tree into a stochastic-based tree with logic gates and long bit streams as its inputs. This kind of tree has an exponential function and conversion of probability to a stochastic bit stream, which uses a random number generator. Moreover, based on probabilistic analysis of dynamic gate i.e., Cold Spare, an accurate model of its stochastic gate is developed. So, implementing it on a HW/SW co-design platform, speeds up the analysis as well as accuracy. The experimental results show that implementing this method on HW/SW co-design is 14 times faster than CPU implementation and 21 times more accurate than previous methods.
Keywords
fault trees; field programmable gate arrays; hardware-software codesign; logic gates; probability; random number generation; reliability theory; Cold Spare; HW/SW codesign; exponential function; fault tree analysis; hardware /software codesign; logic gates; probability conversion; random number generator; reliability; safety; stochastic bit stream; stochastic gate; Fault trees; Hardware; Logic gates; Mathematical model; Reliability; Software; Stochastic processes; Co-design; Fault tree analysis; Field Programmable Gate Array (FPGA); Reliability; Stochastic logic;
fLanguage
English
Publisher
ieee
Conference_Titel
Reliability and Maintainability Symposium (RAMS), 2014 Annual
Conference_Location
Colorado Springs, CO
Print_ISBN
978-1-4799-2847-7
Type
conf
DOI
10.1109/RAMS.2014.6798490
Filename
6798490
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