The Role of EDA in AI (Experts at the Table, Part 3)
By Brian Bailey, Semiconductor Engineering | Feat. Raik Brinkmann, President & CEO, OneSpin
Which aspects of AI implementation should EDA create tools for?
Semiconductor Engineering sat down to discuss the role that EDA has in automating artificial intelligence and machine learning with Doug Letcher, president and CEO of Metrics; Daniel Hansson, CEO of Verifyter; Harry Foster, chief scientist verification for Mentor, a Siemens Business; Larry Melling, product management director for Cadence; Manish Pandey, Synopsys fellow; and Raik Brinkmann, CEO of OneSpin Solutions. What follows are excerpts of that conversation. Part one can be found here. Part two is here.
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Brinkmann: One key piece will be to consider all aspects of how an algorithm goes into an application. That means making sure the platform is trustworthy in multiple ways. You have to trust that the function can be mapped from the software stack, through the various abstraction levels down to the hardware, and that the platform is working as you want it. This is a huge verification problem. Also, is it secure? Can someone tamper with the data on the way there? Can anyone insert malicious code into the platform or into the software.
