System Design and Verification Blogs

Artificial intelligence (AI) is everywhere. Machine learning (ML) and its associated inference abilities promise to revolutionize everything from driving your car to making your breakfast. Verification is never truly complete; it is over when you run out of time. The goal is to make the verification process converge BEFORE you run out of time. Everyone wants to see key metrics converge to target goals and do so within stringent cost and time constraints. Imagine sitting in a cockpit, feeding inputs to the Blackbox, and waiting for the magic to happen (press a button and your job is done). This imagination is closer to reality with Xcelium Machine Learning.

The core focus of Xcelium ML is to examine the regression and identify the relationship between input stimulus and design or functional coverage. Then develop randomized vectors that hit coverage points much more efficiently. So, what do you do when you can achieve the same coverage one-fifth of the time? The answer is quite straightforward – you spend 80 percent of the time you recover finding new bugs in your design.  This is great news for the verification engineer. Finding problems earlier is always a win. Here is a graphic from Intrinsix on what we all know – finding and fixing bugs earlier in the process is much more cost-effective.

If throughput optimization, regression compression, coverage closure, and finding more bugs are your biggest challenges and are giving you sleepless nights, you should check out Xcelium ML.

Xcelium Machine learning can help you combat all the above with increased Verification Efficiency up to 5X. It will help compress your regression and execute only meaningful simulation runs, expose hidden bugs and increase the hit count of rare bins.

You can enjoy even better results, up to 10X, if your environment is ML friendly (meaning has a high degree of randomization in the input state space).

Xcelium Machine Learning offering is available in an attractive pocket-friendly easy-to-use licensing model and fits seamlessly in your existing design verification flow.

In case you missed our previous blog Quest for Bugs – The Constrained-Random Predicament click here. Stay tuned to our next blog which will provide you insights on how to increase the hit count of rare bins using Xcelium Machine Learning.