Processors White Papers
Harnessing Machine Learning to Improve the Success Rate of Stimuli Generation
Overview The initial state of a design under verification has a major impact on the ability of stimuli generators to successfully generate the requested stimuli. For complexity reasons, most stimuli generators use sequential solutions without planning ahead. Therefore, in many cases they fail to produce a consistent stimulus due to an inadequate selection of the initial state. This paper proposes a new method, based on machine learning techniques, to improve generation success by learning the relationship between the initial state vector and generation success. The paper applies the proposed method in two different settings, with the objective of improving generation success and coverage in processor and system level generation. In both settings, the proposed method significantly reduced generation failures and enabled faster coverage.
| Publisher | IBM | File Format | |
|---|---|---|---|
| Date Published | August 2007 | ||
| Format | White Papers | ||
| Topics | |||
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