Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Proceedings of the 43rd annual Design Automation Conference
Artificial Neural Networks
Proceedings of the conference on Design, automation and test in Europe
Statistical performance modeling and optimization
Foundations and Trends in Electronic Design Automation
The impact of random device variation on SRAM cell stability in sub-90-nm CMOS technologies
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Breaking the simulation barrier: SRAM evaluation through norm minimization
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Efficient SRAM failure rate prediction via Gibbs sampling
Proceedings of the 48th Design Automation Conference
Particle Swarm Optimisation: Classical and Quantum Perspectives
Particle Swarm Optimisation: Classical and Quantum Perspectives
Radial Basis Function Networks With Linear Interval Regression Weights for Symbolic Interval Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Massively repeated structures such as SRAM cells usually require extremely low failure rate. This brings on a challenging issue for Monte Carlo based statistical yield analysis, as huge amount of samples have to be drawn in order to observe one single failure. Fast Monte Carlo methods, e.g. importance sampling methods, are still quite expensive as the anticipated failure rate is very low. In this paper, a new method is proposed to tackle this issue. The key idea is to improve traditional importance sampling method with an efficient online surrogate model. The proposed method improves the performance for both stages in importance sampling, i.e. finding the distorted probability density function, and the distorted sampling. Experimental results show that the proposed method is 1e2X~1e5X faster than the standard Monte Carlo approach and achieves 5X~22X speedup over existing state-of-the-art techniques without sacrificing estimation accuracy.