McPOWER: a Monte Carlo approach to power estimation
ICCAD '92 1992 IEEE/ACM international conference proceedings on Computer-aided design
A survey of power estimation techniques in VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
Gate sizing for constrained delay/power/area optimization
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low power electronics and design
Gate Sizing: A General Purpose Optimization Approach
EDTC '96 Proceedings of the 1996 European conference on Design and Test
Probabilistic Treatment of General Combinational Networks
IEEE Transactions on Computers
Gate-level power estimation using tagged probabilistic simulation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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As fast non-simulation-based power estimation techniques, probabilistic simulation techniques were widely researched in the 1990s. Spatial and temporal correlations are commonly known as two fundamental challenges of these kinds of techniques. Previous work showed that spatial correlation could be coped with by means of bit-parallel simulation. For temporal correlation that has great impact on estimating glitches, previous work only showed that it could be considered by means of a glitch-filtering scheme which is an approximation algorithm, but did not answer the question whether temporal correlation could be overcome without any approximation. Our work extends conventional probabilistic simulation techniques and puts the essentials and extensions of probabilistic simulation into a theoretical framework. Based on the framework, this paper shows that modeling temporal correlation in probabilistic simulation without any approximation is only possible in theory. Therefore, an improved approximation of the exact method is proposed. Compared to the conventional probabilistic simulation, our prominently improved results prove the effectiveness of our approximation algorithm. At the end of this paper, the advantages and the bottlenecks of probabilistic simulation are concluded in general.