Philipp Panitz

A gate sizing method for glitch power reduction

Due to the difficulty in estimating dynamic power at the gate level, a quantity called power metric and its efficient calculation method are introduced in this work. Based on the proposed power metric, a heuristic gate sizing algorithm for glitch power reduction is proposed for semi-custom design. The proposed heuristic algorithm minimizes the total power metric of a circuit. According to the experimental results on 8 ISCAS85 benchmark circuits and 5 real industrial circuits, more than 30% average glitch power reduction and 15.5% average total power reduction can be achieved by means of the proposed algorithm, respectively. The achieved improvements on power and area both are more than those by means of conventional gate sizing algorithms.

A theoretical probabilistic simulation framework for dynamic power estimation

As fast non-simulation-based power estimation techniques, probabilistic simulation techniques were widely researched in the 1990s. Spatial and temporal correlations are commonly known as two fundamental challenges of these kinds of techniques. Previous work showed that spatial correlation could be coped with by means of bit-parallel simulation. For temporal correlation that has great impact on estimating glitches, previous work only showed that it could be considered by means of a glitch-filtering scheme which is an approximation algorithm, but did not answer the question whether temporal correlation could be overcome without any approximation. Our work extends conventional probabilistic simulation techniques and puts the essentials and extensions of probabilistic simulation into a theoretical framework. Based on the framework, this paper shows that modeling temporal correlation in probabilistic simulation without any approximation is only possible in theory. Therefore, an improved approximation of the exact method is proposed. Compared to the conventional probabilistic simulation, our prominently improved results prove the effectiveness of our approximation algorithm. At the end of this paper, the advantages and the bottlenecks of probabilistic simulation are concluded in general.