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Intelligate: An Algorithm for Learning Boolean Functions for Dynamic Power Reduction

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In this work we introduce an enhanced methodology to detect dynamic invariants from a power-benchmark simulation trace database. The method is scalable for the application of clock-gating extraction on industrial designs. Our approach focuses upon dynamic simulation data as the main source for detection of opportunities for power reduction. Experimental results demonstrate our ability to learn accurate clock-gating functions from simulation traces and achieve significant power reduction (in the range of 30%–70% of the gated clock net's power) on industrial micro-processor designs.

Keywords: CLOCK GATING; DATA MINING; LOW POWER; MACHINE LEARNING

Document Type: Research Article

Publication date: 01 April 2009

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  • The electronic systems that can operate with very low power are of great technological interest. The growing research activity in the field of low power electronics requires a forum for rapid dissemination of important results: Journal of Low Power Electronics (JOLPE) is that international forum which offers scientists and engineers timely, peer-reviewed research in this field.
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