Nvidia's GPU-powered AI is creating chips with 'better than human design'
Nvidia has been quick to hop on the artificial intelligence bus一with many of its consumer facing technologies, such as Deep Learning Super Sampling (DLSS) and AI-accelerated denoising exemplifying that. However, it has also found many uses for AI in its silicon development process and, as Nvidia's chief scientist Bill Dally said in a GTC conference, even designing new hardware.
Dally outlines a few use cases for AI in its own development process of the latest and greatest graphic cards (among other things), as noted by HPC Wire.
«It’s natural as an expert in AI that we would want to take that AI and use it to design better chips,» Dally says.
«We do this in a couple of different ways. The first and most obvious way is we can take existing computer-aided design tools that we have. For example, we have one that takes a map of where power is used in our GPUs, and predicts how far the voltage grid drops一what’s called IR drop for current times resistance drop. Running this on a conventional CAD tool takes three hours.»
"...what we’d like to do instead is train an AI model to take the same data; we do this over a bunch of designs, and then we can basically feed in the power map. The inference time is just three seconds. Of course, it’s 18 minutes if you include the time for feature extraction.
"...we’re able to get very accurate power estimations much more quickly than with conventional tools and in a tiny fraction of the time," Dally continues.
Dally mentions other ways AI can be handy for developing next-generation chips. One is in predicting parasitics, which are essentially unwanted elements in components or designs that could be inefficient or simply cause something to not work as intended. Rather than use human work
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