Power invariant vector compaction based on bit clustering and temporal partitioning

Dragone, Nicola; Zafalon, R.; Guardiani, Carlo; Silvano, Cristina

doi:10.1145/280756.280819

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Input signal entropy was proposed in [20] and applied to power macromodeling in [13]. Temporal correlation was introduced in [12] and applied to power macromodeling in [5]. In this paper, we choose average input signal probability p [4,15,18], average input transition density d [4,15], and input spatial correlation s [4,15] as the input parameters to generate the macromodels.…”

Section: Power Macromodeling Characterizationmentioning

confidence: 99%

A Markov Chain Sequence Generator for Power Macromodeling

Liu

Papaefthymiou

2004

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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In this paper, we present a novel sequence generator based on a Markov chain model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints derived from the specified statistics. We also give a practical heuristic that computes such a matrix and generates a sequence of l n-bit vectors in O nlDerived from a strongly mixing Markov chain, our generator yields binary vector sequences with accurate statistics, high uniformity, and high randomness. Experimental results show that our sequence generator can cover more than 99% of the parameter space. Sequences of 2,000 48-bit vectors are generated in less than 0.05 seconds, with average deviations of the signal statistics p, d, and s equal to 1.6%, 1.8%, and 2.8%, respectively.Our generator enables the detailed study of power macromodeling. Using our tool and the ISCAS-85 benchmark circuits, we have assessed the sensitivity of power dissipation to the three input statistics p, d, and s. Our investigation reveals that power is most sensitive to transition density, while only occasionally exhibiting high sensitivity to signal probability and spatial correlation. Our experiments also show that input signal imbalance can cause estimation errors as high as 100% in extreme cases, although errors are usually within 25%.

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Section: Power Macromodeling Characterizationmentioning

confidence: 99%

A Markov Chain Sequence Generator for Power Macromodeling

Liu

Papaefthymiou

2004

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…These features include preserving the pattern transition probabilities, 1 preserving the spatial-temporal correlations for all inputs, 2 and preserving the significant correlations between the clustered inputs. 3 Regenerating a compact input sequence sounds easy. However, the compact input sequence can only be generated according to a user-specified compaction ratio, which users usually do not know the proper value.…”

Section: Introductionmentioning

confidence: 99%

Bootstrap Monte Carlo With Adaptive Stratification for Power Estimation

Huang

Jou

2002

J CIRCUIT SYST COMP

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Monte Carlo approach for power estimation is based on the assumption that the samples of power are Normally distributed. However, the power distribution of a circuit is not always Normal in the real world. In this paper, the Bootstrap method is adopted to adjust the confidence interval and redeem the deficiency of the conventional Monte Carlo method. Besides, a new input sequence stratification technique for power estimation is proposed. The proposed technique utilizes a multiple regression method to compute the coefficient matrix of the indicator function for stratification. This new stratification technique can adaptively update the coefficient matrix and keep the population of input vectors in a better stratification status. The experimental results demonstrate that the proposed Bootstrap Monte Carlo method with adaptive stratification can effectively reduce the simulation time and meet the user-specified confidence level and error level.

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“…The main issue in the definition of Ti, is the choice of L, since a value that is either too large or too small may result in missing correlation information. In [2] the authors use a similar window technique to estimate the temporal correlation of input streams with arbitrary distributions. Their study shows that a suitable value for L is 10.…”

Section: Macromodel Metricsmentioning

confidence: 99%

Analytical macromodeling for high-level power estimation

Bernacchia

Papaefthymiou

1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051)

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This paper presents a new macromodeling technique for high-level power estimation. Our technique is based on a parameterizable analytical model that relies exclusively on statistical information of the circuit's primary inputs. During estimation, the statistics of the required metrics are extracted from the input stream, and a power estimate is obtained by evaluating a model function that has been characterized in advance. Our model yields power estimates within seconds, because it does not rely on the statistics of the circuit's primary outputs and, consequently, does not perform any simulation during estimation. Moreover, it achieves better accuracy than previous macromodeling approaches by taking into account both spatial and temporal correlations in the input stream.In experiments with the ISCAS-85 combinational circuits, the average absolute relative error of our power macromodeling technique was at most 1.8%. The worst-case error was at most 12.8%. For a ripple-carry adder family, in comparison with power estimates that were obtained using Spice, the average absolute and worst-case errors of our model's estimates were at most 5.1% and 19.8%, respectively.In addition to power dissipation, our macromodeling technique can be used to estimate the statistics of a circuit's primary outputs with very low average errors. It is thus suitable for power estimation in core-based systems with pre-characterized blocks. Once the metrics of the primary inputs are known, the power dissipation of the entire system can be estimated by simply propagating this information through the blocks using their corresponding model functions.

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Power invariant vector compaction based on bit clustering and temporal partitioning

Cited by 7 publications

References 15 publications

A Markov Chain Sequence Generator for Power Macromodeling

A Markov Chain Sequence Generator for Power Macromodeling

Bootstrap Monte Carlo With Adaptive Stratification for Power Estimation

Analytical macromodeling for high-level power estimation

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