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2012
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A fast estimation of SRAM failure rate using probability collectives

8 years 7 months ago
A fast estimation of SRAM failure rate using probability collectives
Importance sampling is a popular approach to estimate rare event failures of SRAM cells. We propose to improve importance sampling by probability collectives. First, we use “Kullback-Leibler (KL) distance” to measure the distance between the optimal sampling distribution and the original sampling distribution of variable process parameters. Further, the probability collectives (PC) technique using immediate sampling is adapted to analytically minimize the KL distance and to obtain a sampling distribution as close to the optimal as possible. The proposed algorithm significantly accelerates the convergence of importance sampling. Experiments demonstrate that proposed algorithm is 5200X faster than the Monte Carlo approach and achieves more than 40X speedup over other existing state-of-the-art techniques without compromising estimation accuracy. Categories and Subject Descriptors: B.7.[Hardware]: Integrated Circuits-Design Aids General Terms: Algorithms, Verification
Fang Gong, Sina Basir-Kazeruni, Lara Dolecek, Lei
Added 25 Apr 2012
Updated 25 Apr 2012
Type Journal
Year 2012
Where ISPD
Authors Fang Gong, Sina Basir-Kazeruni, Lara Dolecek, Lei He
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