Hardware verification currently takes more than 50% of the whole verification time. There is a sustained effort to improve the efficiency of the verification process, which in the past helped deliver a large variety of supporting tools. The past years though did not see any major technology change that would bring the improvements that the process really needs (H. Foster 2013) (Wilson Research Group 2012). The existing approach to verification does not provide that type of qualitative jump anymore. This work is introducing a new tactic, providing a modern alternative to the existing approach to the verification problem. The novel approach I use in this research has the potential of significantly improve the process, way beyond incremental changes. It starts with acknowledging the huge amounts of data that follows the hardware development process from inception to the final product and in considering the data not as a quantitative by-product but as a qualitative supply of information on which we can develop a smarter verification. The approach is based on data already generated throughout the process currently used by verification engineers to zoom into the details of different verification aspects. By using existing machine learning approaches we can zoom out and use the same data to extract information, to gain knowledge that we can use to guide the verification process. This approach allows an apparent lack of accuracy introduced by data discovery, to achieve the overall goal. The latest advancements in machine learning and data mining offer a base of a new understanding and usage of the data that is being passed through the process. This work takes several practical problems for which the classical verification process reached a roadblock, and shows how the new approach can provide a jump in productivity and efficiency of the verification process. It focuses on four different aspects of verification to prove the power of this new approach:
reducing effort redundancy, guiding verification to areas that need it first, decreasing time to diagnose, and designing tests for coverage efficiency. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/28391 |
| Date | 10 February 2015 |
| Creators | Farkash, Monica C. |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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