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Archive for the ‘Mentor’ Category

Post-silicon SW Debug, AI/ML and SSD Design Verification all at Mentor U2U 2019

Wednesday, May 22nd, 2019

Mentor U2U in Santa Clara was a particularly interesting event for the emulation division at Mentor. This year, our customers from Marvell, SK hynix, Wave Computing, Broadcom, and AMD talked about their challenges and successes – bringing to other engineers the information they need to apply best practices. If you couldn’t be there in person on May 2, here are a few things to ask yourself.

  • Does the AI/ML design you’re working on need a verification platform that can optimize HW and SW together by running frameworks and performance benchmarks?
  • If you’re designing SSDs, do you want to have access to high-performance memory solutions that allow you to model specific memory types?
  • Are you struggling to reproduce bugs with simulation or FPGA prototyping?
  • When you are doing simulation acceleration, are you faced with the limitations of an interface-based BFM architecture?
  • Are you frustrated by the shortcomings of a verification environment that doesn’t give you the ability to determine memory tuning settings earlier in the design and verification schedule?


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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