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Challenges in Using AI Verification

By: Ann Steffora Mutschler

Just getting to the right starting point is problematic, and it doesn’t get easier from there.

Sergio Marchese, technical marketing manager at OneSpin Solutions, noted there are two main sources of data in verification. The first is the data that EDA companies have. The second is generated by an organization from a specific project. “You need both to develop robust solutions. Many modern tools have already started to leverage both sets of data,” he said.

Along with other EDA providers, OneSpin has accumulated an enormous amount of test cases over the past 20 years, beginning from its genesis in Siemens R&D. “If you consider the slow evolution of RTL coding, these test cases cover a considerable number of modern hardware coding patterns. OneSpin uses ML algorithms to analyze a large number of design characteristics and automatically configure proof strategies. This reduces the need for users to learn and fiddle with the details of the proof engines parameters and speed up proofs automatically,” Marchese explained.

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Finally, if there are a few months before tape-out, users could get help to find bugs they didn’t anticipate, and that are not expressed in coverage goals. “A lot of that is going to be data in the engines. It is going to be new data analytics platforms. But it’s also going to be creating new linkages such as time requirements into test planning, into verification management, and linking those together for full visibility. If you’re building a system of systems, or even just a large system, you suddenly can see an unacceptable risk because three or four levels down in the requirements linked to a complex test plan, linked to a set of coverage goals, there’s a discrepancy. That kind of data becomes really interesting and incredibly valuable.

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