IEICE TRANSACTIONS on Fundamentals
Vector Compaction Using Dynamic Markov Models
Summary :
This paper presents an effective and robust technique for compacting a large sequence of input vectors into a much smaller input sequence so as to reduce the circuit/gate level simulation time by orders of magnitude and maintain the accuracy of the power estimates. In particular, this paper introduces and characterizes a family of dynamic Markov trees that can model complex spatiotemporal correlations which occur during power estimation both in combinational and sequential circuits. As the results demonstrate, large compaction ratios of 1-2 orders of magnitude can be obtained without significant loss (less than 5% on average) in the accuracy of power estimates.
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- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.10 pp.1924-1933
- Publication Date
- 1997/10/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
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Radu MARCULESCU, Diana MARCULESCU, Massoud PEDRAM, "Vector Compaction Using Dynamic Markov Models" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 10, pp. 1924-1933, October 1997, doi: .
Abstract: This paper presents an effective and robust technique for compacting a large sequence of input vectors into a much smaller input sequence so as to reduce the circuit/gate level simulation time by orders of magnitude and maintain the accuracy of the power estimates. In particular, this paper introduces and characterizes a family of dynamic Markov trees that can model complex spatiotemporal correlations which occur during power estimation both in combinational and sequential circuits. As the results demonstrate, large compaction ratios of 1-2 orders of magnitude can be obtained without significant loss (less than 5% on average) in the accuracy of power estimates.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_10_1924/_p
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@ARTICLE{e80-a_10_1924,
author={Radu MARCULESCU, Diana MARCULESCU, Massoud PEDRAM, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Vector Compaction Using Dynamic Markov Models},
year={1997},
volume={E80-A},
number={10},
pages={1924-1933},
abstract={This paper presents an effective and robust technique for compacting a large sequence of input vectors into a much smaller input sequence so as to reduce the circuit/gate level simulation time by orders of magnitude and maintain the accuracy of the power estimates. In particular, this paper introduces and characterizes a family of dynamic Markov trees that can model complex spatiotemporal correlations which occur during power estimation both in combinational and sequential circuits. As the results demonstrate, large compaction ratios of 1-2 orders of magnitude can be obtained without significant loss (less than 5% on average) in the accuracy of power estimates.},
keywords={},
doi={},
ISSN={},
month={October},}
Copy
TY - JOUR
TI - Vector Compaction Using Dynamic Markov Models
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1924
EP - 1933
AU - Radu MARCULESCU
AU - Diana MARCULESCU
AU - Massoud PEDRAM
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1997
AB - This paper presents an effective and robust technique for compacting a large sequence of input vectors into a much smaller input sequence so as to reduce the circuit/gate level simulation time by orders of magnitude and maintain the accuracy of the power estimates. In particular, this paper introduces and characterizes a family of dynamic Markov trees that can model complex spatiotemporal correlations which occur during power estimation both in combinational and sequential circuits. As the results demonstrate, large compaction ratios of 1-2 orders of magnitude can be obtained without significant loss (less than 5% on average) in the accuracy of power estimates.
ER -
English
