Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Estimation of average switching activity in combinational and sequential circuits
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Switching activity analysis considering spatiotemporal correlations
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Improving the accuracy of circuit activity measurement
DAC '94 Proceedings of the 31st annual Design Automation Conference
Efficient estimation of dynamic power consumption under a real delay model
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Dependency preserving probabilistic modeling of switching activity using bayesian networks
Proceedings of the 38th annual Design Automation Conference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Fast Power Estimation of Large Circuits
IEEE Design & Test
Average Power in Digital CMOS Circuits using Least Square Estimation
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Probabilistic modeling of dependencies during switching activity analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Gate-level power estimation using tagged probabilistic simulation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Switching activity estimation is a crucial step in estimating dynamic power consumption in CMOS circuits. In [1] , we proposed a new switching probability model based on Bayesian Networks which captures accurately the various correlations in the circuit. In this work, we propose a new strategy for efficient segmentation of large circuits so that they can be mapped to Multiple Bayesian Networks (MBN). The goal here is to achieve higher accuracy while reducing the memory requirements during the computation. In order to capture the correlations among the boundaries of segments, a tree-dependent (TD) distribution is proposed between the segment boundaries such that the TD distribution is closest to the actual distribution of switching variable with some distance criterion. We use a Maximum Weight Spanning Tree (MWST) based approximation [4] using mutual information between two variables at the boundary as weight of the edge between the variables. Experimental results for ISCAS'85 circuits show that the proposed method improves accuracy significantly over other methods.