VLSI Design

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Low Power Architecture and Circuit Design Computer Aided Design

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Volume 12 |Article ID 073872 | https://doi.org/10.1155/2001/73872

Miriam Leeser, Valerie Ohm, "Accurate Power Estimation for Sequential CMOS Circuits Using Graph-based Methods", VLSI Design, vol. 12, Article ID 073872, 17 pages, 2001. https://doi.org/10.1155/2001/73872

Accurate Power Estimation for Sequential CMOS Circuits Using Graph-based Methods

Received20 Jun 2000
Revised03 Aug 2000


We present a novel method for estimating the power of sequential CMOS circuits. Symbolic probabilistic power estimation with an enumerated state space is used to estimate the average power switched by the circuit. This approach is more accurate than simulation based methods. Automatic circuit partitioning and state space exploration provide improvements in run-time and storage requirements over existing approaches. Circuits are automatically partitioned to improve the execution time and to allow larger circuits to be processed. Spatial correlation is dealt with by minimizing the cutset between partitions which tends to keep areas of reconvergent fanout in the same partition. Circuit partitions can be recombined using our combinational estimation methods which allow the exploitation of knowledge of probabilities of the circuit inputs. We enumerate the state transition graph (STG) incrementally using state space exploration methods developed for formal verification. Portions of the STG are generated on an as-needed basis, and thrown away after they are processed. BDDs are used to compactly represent similar states. This saves significant space in the storage of the STG. Our results show that modeling the state space is imperative for accurate power estimation of sequential circuits, partitioning saves time, and incremental state space exploration saves storage space. This allows us to process larger circuits than would otherwise be possible

Copyright © 2001 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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