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We propose a novel power macro-model which is based on the Hamming-distance of two consecutive input vectors and additional information on the module structure. The model is parameterizable in terms of input bit-widths and can be applied to a wide variety of datapath components. The good trade-off between estimation accuracy, model complexity and flexibility makes the model attractive for power analysis and optimization tasks on a high level of abstraction. Furthermore, a new approach is presented, that allows to calculate the average Hamming-distance distribution of an input data stream. It will be demonstrated, that the application of Hamming-distance distributions, instead of only average values, improves the estimation accuracy for a number of typical DSP-modules and data streams.

Kruse@OFFIS.Uni-Oldenburg.DE 1. ABSTRACT In this paper we present an approach to calculate lower and upper bounds for the switching activity in scheduled data flow graphs. The technique can be used to prune the design space in high level synthesis for low power before allocation and binding of functional units and registers. The low power allocation and binding problem is formulated. It is shown that this problem can be relaxed to the bipartite weighted matching problem which is solvable in O(n3) where n is the number of functional units or registers, respectively. The application of the technique on benchmarks shows the tightness of the bounds. Most of the investigated bounds were less than 1% off the minimum respectively maximum solutions.

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The problem of estimating lower bounds on the power consumption in scheduled data flow graphs with a fixed number of allocated resources prior to binding is addressed. The estimated bound takes into account the effects of resource sharing. It is shown that by introducing Lagrangian multipliers and relaxing the low power binding problem to the Assignment Problem, which can be solved in , a tight and fast computable bound is achievable. Experimental results show the good quality of the bound. In most cases, deviations smaller than 5% from the optimal binding were observed. The proposed technique can for example be applied in branch and bound high-level synthesis algorithms for efficient pruning of the design space.