The number of companies using machine learning is accelerating, but so far there are no tools to validate, verify and debug these systems. That presents a problem for the chipmakers and systems companies that increasingly rely on machine learning to optimize their technology because, at least for now, it creates the potential for errors that are extremely difficult to trace and fix. At the same time, it opens up new opportunities for companies that have been developing static tools to expand their reach well beyond just the chip, where profits are being squeezed by system vendors. But as shown in part one of this series, that will take years rather than months to fix. Research is just beginning on how to tackle these problems, let alone develop comprehensive tool suites. “Across the board, machine learning is suddenly becoming very interesting,” said Sundari Mitra, CEO and co-founder of NetSpeed Systems. “If you look at the entire EDA industry in terms of machine learning, what it has gotten us is synthesis. As we get more people coming out with a background in machine learning, with degrees that specialize in this, they will get drawn into fixing some of these things. I see a movement… Read full this story
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