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Using Hardware Emulation to Verify AI Designs

Wednesday, March 6th, 2019

You can’t turn around these days without seeing a reference to AI – even as a consumer. AI, or artificial intelligence, is hot due to the new machine-learning (ML) techniques that are evolving daily. It’s often cited as one of the critical markets for electronics purveyors, but it’s not really a market: it’s a technology. And it’s quietly – or not so quietly – moving into many, many markets. Some of those markets include safety-critical uses, meaning that life and limb can depend on how well it works.

AI is incredibly important, but it differs from many other important technologies in how it’s verified.

Three Key Requirements

AI/ML verification brings with it three key needs: determinism, scalability, and virtualization. These aren’t uncommon hardware emulation requirements, but many other technologies require only two out of those three. AI is the perfect storm that needs all three.

ML involves the creation of a model during what is called the “training phase” – at least in its supervised version. That model is then implemented in a device or in the cloud for inference, where the trained model is put to use in an application.

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TrueCircuits: IoTPLL



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