Application-driven processor design exploration for power-performance trade-off analysis
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Cosimulation-based power estimation for system-on-chip design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Compacting sequences with invariant transition frequencies
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A Markov chain sequence generator for power macromodeling
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Dynamic Functional Unit Assignment for Low Power
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Causal probabilistic input dependency learning for switching model in VLSI circuits
GLSVLSI '05 Proceedings of the 15th ACM Great Lakes symposium on VLSI
Dynamic functional unit assignment for low power
The Journal of Supercomputing
Proceedings of the 41st annual Design Automation Conference
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Power estimation has become a critical step in the design of today's integrated circuits (ICs). Power dissipation is strongly input pattern dependent and, hence, to obtain accurate power values one has to simulate the circuit with a large number of vectors that typify the application data. The goal of this paper is to present an effective and robust technique for compacting large sequences of input vectors into much smaller ones such that the power estimates are as accurate as possible and the simulation time is reduced by orders of magnitude. Specifically, this paper introduces the hierarchical modeling of Markov chains as a flexible framework for capturing not only complex spatiotemporal correlations, but also dynamic changes in the sequence characteristics. In addition to this, we introduce and characterize a family of variable-order dynamic Markov models which provide an effective way for accurate modeling of external input sequences that affect the behavior of finite state machines. The new framework is very effective and has a high degree of adaptability. As the experimental results show, large compaction ratios of orders of magnitude can be obtained without significant loss in accuracy (less than 5% on average) for power estimates