Graduation Year

2004

Document Type

Thesis

Degree

M.S.E.E.

Degree Granting Department

Electrical Engineering

Major Professor

Sanjukta Bhanja, Ph.D.

Committee Member

Yun-Leei Chiou, Ph.D.

Committee Member

Wilfrido A. Moreno, Ph.D.

Keywords

Gate Level, Bayesian Network, Simulation, Sampling, Entropy

Abstract

Power dissipation in a VLSI circuit poses a serious challenge in present and future VLSI design. A switching model for the data dependent behavior of the transistors is essential to model dynamic, load-dependent active power and also leakage power in active mode - the two components of power in a VLSI circuit. A probabilistic Bayesian Network based switching model can explicitly model all spatio-temporal dependency relationships in a combinational circuit, resulting in zero-error estimates. However, the space-time requirements of exact estimation schemes, based on this model, increase with circuit complexity [5, 24]. This work explores a non-simulative, importance sampling based, probabilistic estimation strategy that scales well with circuit complexity. It has the any-time aspect of simulation and the input pattern independence of probabilistic models. Experimental results with ISCAS'85 benchmark shows a significant savings in time (nearly 3 times) and significant reduction in maximum error (nearly 6 times) especially for large benchmark circuits compared to the existing state of the art technique (Approximate Cascaded Bayesian Network) which is partition based. We also present a novel probabilistic method that is not dependent on the pre-specification of input-statistics or the availability of input-traces, to identify nodes that are likely to be leaky even in the active zone. This work emphasizes on stochastic data dependency and characterization of the input space, targeting data-dependent leakage power. The central theme of this work lies in obtaining the posterior input data distribution, conditioned on the leakage at an individual signal. We propose a minimal, causal, graphical probabilistic model (Bayesian Belief Network) for computing the posterior, based on probabilistic propagation flow against the causal direction, i.e. towards the input. We also provide two entropy-based measures to characterize the amount of uncertainties in the posterior input space as an indicator of the likelihood of the leakage of a signal. Results on ISCAS'85 benchmark shows that conclusive judgments can be made on many nodes without any prior knowledge about the input space.

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