“Especially when you look at the chip design itself, it was much more a, let's start in the digital, let's freeze the digital in, let's send it through some physics for a one time check, like the PDK dictates, I have to have The following feature, otherwise TSNC doesn't make it for you. And it stops there. Like, it's like, ooh, do we have enough headroom to push it through? But we're not taking advantage today of that headroom to get extra performance.”
quote is from the automated transcript, cleaned for reading:
filler sounds and stutters are removed, nothing is rephrased. names can be misheard
(the analysis reads context, assessments check outside sources). how →
“Like we're able to train up to a trillion context input. We're able to train With like, even inputs, outputs, both trillion context length, we are able to do inference at five trillion contexts.”
Jenik: PDE feedback pushes physics AI models to exceed training data
“You can use those PDEs both for numerical simulators to generate data, but if you're clever about it, you can even use them as a training signal. You can check how well is my model actually doing on the PDEs themselves. And use that as an additional training s…”
Jenik: Broadening PDE training domains solves the physical sim-to-real gap
“One interesting thing with PDEs is we are actually fairly confident, like the math is known, that when you solve a PDE correctly, you're doing the physics correctly. Like, obviously, you still need to make sure that you're representing the task that you're try…”
Jenik: Accelerated Understanding's medium-sized physics models run inference on Mac Studios
“We have done inference on, like, we can do small models with smaller context, or even medium big models with smaller context fit on a MacBook or Mac Studio.”
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