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The estimation of average-power dissipation of a circuit through exhaustive simulation is impractical due to the large number of primary inputs and their combinations. In this brief, two algorithms based on least square estimation are proposed for determining the average power dissipation in complementary metal-oxide-semiconductor (CMOS) circuits. Least square estimation converges faster by attempting to minimize the mean square error value during each iteration. Two statistical approaches namely, the sequential least square (SLS) estimation and the recursive least square estimation are investigated. The proposed methods are distribution independent in terms of the input samples, unbiased and point estimation based. Experimental results presented for the MCNC'91 and the ISCAS'89 benchmark circuits show that the least square estimation algorithms converge faster than other statistical techniques such as the Monte Carlo method [4] and the DIPE [8].

In this paper, we describe a new methodology based on game theory for minimizing the average power of a circuit during scheduling in behavioral synthesis. The problem of scheduling in data-path synthesis is formulated as an auction based non-cooperative finite game, for which solutions are developed based on the Nash equilibrium function. Each operation in the data-path is modeled as a player bidding for executing an operation in the given control cycle, with the estimated power consumption as the bid. Also, a combined scheduling and binding algorithm is developed using a similar approach in which the two tasks are modeled together such that the Nash equilibrium function needs to be applied only once to accomplish both the scheduling and binding tasks together. The combined algorithm yields further power reduction due to additional savings during binding. The proposed algorithms yield better power reduction than ILP-based methods with comparable run times and no increase in area overhead.

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