61–70 of 81 posts
I work on this team! (Specifically: applied deep learning research, chip design). It's a shame to see so many people dismissing this work as marketing. I see lots of clever people working hard on really novel and interesting stuff, and I really do think that ML has real potential to customize a design much more "deeply" than traditional automation tools.
I work on this team! (Specifically: applied deep learning research, chip design). It's a shame to see so many people dismissing this work as marketing. I see lots of clever people working hard on really novel and interesting stuff, and I really do think that ML has real potential to customize a design much more "deeply" than traditional automation tools.
Earlier quoted context omitted.
Please keep in mind that even the slightest error in a silicon mask is likely to cause hundreds of millions of dollars of losses and months of delay in time to market for a modern chip. With that in mind, does it make more sense to come up with new, experimental, untested algorithms... or just use wider numbers and slowly iterate on well known algorithms? Especially with LVS/DRC you really want the dumbest, easiest t…
The exact same arguments were made by CAD people insisting on 64-bit maths for OpenGL. They were wrong. They too were working on projects worth billions of dollars, over decades, where mistakes were very costly. Your link to a "DRC set" doesn't mean much to me out of context. I see some basic looking code with small-ish numeric constants in it. So what? This is not that different to the input to a simple physics simu…
For somewhat obvious reasons, we have a vested interest in this never occurring. So we default to safety over speed. Meanwhile in the CAD world with 64-bit math not making it into OpenGL, they just wrote a library to do 64-bit math anyways on-top of or in parallel to OpenGL. They didn't switch away from 64-bit math, they just reduced its use where it isn't needed and kept it where it is needed. The semiconductor industry is full of absolutely brilliant engineers who know far too much about all of the problems and if they could use 64-bit instead of 128-bits for a data structure, they'd switch in a heartbeat to save massive amounts of compute time (and thus money).
Earlier quoted context omitted.
> but it's also extremely NVidia that they're doing it all on their own. Having a lead in chip design is their literal bread and butter. I think it's extremely "publicly traded company" more than "NVidia". Do you have an example of a company releasing an open source version of their secret sauce (foundation of their profits)?
> Do you have an example of a company releasing an open source version of their secret sauce? The chip design itself should be the secret sauce. Not the tools you make the chip with. Nvidia is resolutely not-contributing. Many other companies are starting to get onboard with open chip design. This doesn't mean the chips have to be open, but the tooling needs to be something shared & co-developable. If this is a littl…
The chip comes from the chip design, and the chip design is made with tools. None can exist alone.
> Many other companies are starting to get onboard with open chip design.
> There are plenty of company's releasing the chips too. Google's OpenTitan[2] security chip. WD's Swerv RISC-V core for their driver controller ARM R-series replacement[3].
These chips aren't the foundation of Google or WD's revenue stream. You won't see them significantly affecting a line item in their quarterly reports.
> are any companies other than Nvidia embarking up AI/ML chipmaking in a closed fashion?
Nvidia is in a unique position where the foundation of their profits (chips) happens to be what makes practical AI possible. They're literally running the vast majority of the show. If something falls into the "foundation of existence" circle in their Venn diagram of concerns, they're going to be less open about it. Improving the ability to design chips is at the exact center of that "foundation of existence" circle.
Earlier quoted context omitted.
> The chip design itself should be the secret sauce. Not the tools you make the chip with. I’m sure Cadence, Synopsys, and Mentor would love to hear more about this.
Everyone keeps hammering home how much of the process is proprietary. Whose interest is that in though? Is it in Nvidias & Intels & Qualcomm's interest to let these chip design software companies have extremely proprietary cake, that no one can advance or enhance, that has no machine-learning capabilities surrounding it? To me it feels like so many are missing the picture here. Chip designers ought to cooperate on to…
Earlier quoted context omitted.
> but it's also extremely NVidia that they're doing it all on their own. Having a lead in chip design is their literal bread and butter. I think it's extremely "publicly traded company" more than "NVidia". Do you have an example of a company releasing an open source version of their secret sauce (foundation of their profits)?
> Do you have an example of a company releasing an open source version of their secret sauce? The chip design itself should be the secret sauce. Not the tools you make the chip with. Nvidia is resolutely not-contributing. Many other companies are starting to get onboard with open chip design. This doesn't mean the chips have to be open, but the tooling needs to be something shared & co-developable. If this is a littl…
What is extremely telling is what is missing ... Design Rule Checking (DRC) and Layout Vs Schematic (LVS). These require: 1) Longer bit length arithmetic 32-bit float simply isn't enough. 64-bit float is close, but limited. You really want 128-bit integer. And nVidia isn't delivering that. 2) Real algorithmic improvements We're still stuck with computational geometry algorithms that don't parallelize. It would be awf…
DRC and LVS are just logical checks right? “Is the minimal distance between all metal routing > 10 nm” etc. Can you explain why high precision is needed for that?
I'm less confident about it when it comes to anything that involves calculating anything electromagnetic because I just don't know that subfield.
Earlier quoted context omitted.
Everyone keeps hammering home how much of the process is proprietary. Whose interest is that in though? Is it in Nvidias & Intels & Qualcomm's interest to let these chip design software companies have extremely proprietary cake, that no one can advance or enhance, that has no machine-learning capabilities surrounding it? To me it feels like so many are missing the picture here. Chip designers ought to cooperate on to…
The skills needed to create a chip and the skills needed to create chip design software are fundamentally different. Of all the engineers I've met who work on the physical implementation and timing closure of digital chips, only a very limited number would have any hope of creating some sort of place and route tool, and it would be rudimentary and inefficient. They are not expert programmers.
There's another nice upstart opensource project with even fancier ml placememt systems that spawned recently out of the openroad world, dreamplace, https://github.com/limbo018/DREAMPlace
This is just gonna get more & more biased against a couple super smart engineers who we've deeply entrusted to divine inner the workings of the chips on, & become increasingly a set of better modelled problems that we can machine learningly optimize.
I work on this team! (Specifically: applied deep learning research, chip design). It's a shame to see so many people dismissing this work as marketing. I see lots of clever people working hard on really novel and interesting stuff, and I really do think that ML has real potential to customize a design much more "deeply" than traditional automation tools.
I think it's funny how "the old AI" had combinatorical optimization as a major theme, for instance https://en.wikipedia.org/wiki/Travelling_salesman_problem which is closely related to the central operation of logic, the canonical NP problem https://en.wikipedia.org/wiki/Boolean_satisfiability_problem as well as the playing of games like Chess, Poker, etc. Modern neural networks also have optimization as a theme even…