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Did nvidia just promise us singularity? :) Hard to read a talk like this from a pulpit & not see shout outs to the incredibly super-fantastic open-source innov

Did nvidia just promise us singularity? :) Hard to read a talk like this from a pulpit & not see shout outs to the incredibly super-fantastic open-source innovative projects like OpenROAD which have been shipping amazingly well-routed-by-AI chips for a while now. There's papers you can cite, galore, many open source designs[1]. It's not like Nvidia is promising anyone else will benefit from this work. This seems to be very high level coverage their R&D department is looking at, perhaps/perhaps not using. The article makes it hard to find out what is available, what has been published or otherwise deeply discussed (which is I think the best we can hope from Nvidia not real participation). There's only one paper linked, on NVCell[2], described as: > The first is a system we have called NVCell, which uses a combination of simulated annealing and reinforcement learning to basically design our standard cell library. This just feels like so much else going on in computing. WSL coming to windows, the recent Unity vs Unreal topic[3]. It's hard to imagine refusing to participate with others. It's hard to imagine not being part of the open source community working shoulder to shoulder to push for better. NVidia patently doesn't get it, patently isn't participating, patently isn't there. It's cool we can hear what they are up to, but it's also extremely NVidia that they're doing it all on their own. Anyhow, Looking forward to more AI based chip power system design starting to emerge; that sounds like a good idea NV. [1] https://theopenroadproject.org/ https://theopenroadproject.org/ [2] https://research.nvidia.com/publication/2021-12_nvcell-standard-cell-layout-advanced-technology-nodes-reinforcement-learning https://research.nvidia.com/publication/2021-12_nvcell-stand... [3] https://news.ycombinator.com/item?id=31064552 https://news.ycombinator.com/item?id=31064552 (412 points, 3 days ago, 311 comments)

nomel 5y ago
> 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)?
- bawolff 5y ago
  Netscape. Worked out great for them /s (albeit writing was already on the wall for them by that point.)
- rektide 5y ago
  > 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 little pet research project that's one thing, but there really needs to be ongoing workforce development, a strong advance. The NSF's TILOS, a strong alliance/nexus of researchers within & around the OpenROAD community, get this[1]: > TILOS – The Institute for Learning-enabled Optimization at Scale – is an NSF National AI Research Institute for advances in optimization, partially supported by Intel Corporation. The institute began operations in November 2021 with a mission to "make impossible optimizations possible, at scale and in practice". > There are six universities in TILOS: UCSD, MIT, National University, Penn, UT-Austin, and Yale. The institute seeks a new nexus of AI and machine learning, optimization, and use in practice. Figure 4 shows four virtuous cycles envisioned for the institute: 1. mutual advances of AI and optimization provide the foundations; 2. challenges of scale, along with breakthroughs from scaling, bind together foundations and the use domains of chip design, networks and robotics; 3. the cycle of translation and impact brings research and the leading edge of practice closer together; and 4. the cycle of research, education, and broadening participation grows the field and its workforce. The virtues written here are self evident & obvious. Trying to just get good yourself without trying to help advance the field, not participating, not taking advantages of scale of many working together, not participating in open research, the risks of having isolated teams, and not participating in cycles of development: whatever the nvidia or "publicly traded company" worlds think they're doing, they're missing out, and hurting everyone and especially themselves for this oldschool zero-sum competitive thinking. 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]. Open standards if not chips like UCI for chiplets or CXL for interconnect are again examples of literally everyone but NVidia playing well together, trying for better, standardizing a future for participation & healthy competition & growth. Nvidia again and again is the company which simply will not play with others. I challenge you to answer your own question in reverse: are any companies other than Nvidia embarking up AI/ML chipmaking in a closed fashion? There probably are, let's follow & watch them. [1] https://theopenroadproject.org/news/leveling-up-a-trajectory-of-openroad-tilos-and-beyond/ https://theopenroadproject.org/news/leveling-up-a-trajectory... [2] https://opentitan.org/ https://opentitan.org/ [3] https://github.com/chipsalliance/Cores-SweRV https://github.com/chipsalliance/Cores-SweRV
jwlake 5y ago
That article doesn't actually have enough data to really know. The anecdotes are all just design assist, make things faster from 20 mins to 3 seconds. Not singularity. There are teasers but nothing clear singularity. I think the big problem is they are just using it for fuzzy algorithm optimization, which is clearly not self learning.
rrss 5y ago
which openroad tools use "AI" rather than normal optimization like traditional EDA tools have used for decades?
areskay3 5y ago
All of the work discussed is published to some extent. References are in the slides https://arxiv.org/pdf/2012.10597.pdf https://arxiv.org/pdf/2012.10597.pdf https://research.nvidia.com/publication/2020-07_grannite-graph-neural-network-inference-transferable-power-estimation https://research.nvidia.com/publication/2020-07_grannite-gra... https://research.nvidia.com/sites/default/files/pubs/2020-07_ParaGraph:-Layout-Parasitics/057_4_Paragraph.pdf https://research.nvidia.com/sites/default/files/pubs/2020-07... https://ieeexplore.ieee.org/document/8920342 https://ieeexplore.ieee.org/document/8920342

nomel 5y ago

> 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)?

bawolff 5y ago
Netscape. Worked out great for them /s (albeit writing was already on the wall for them by that point.)
rektide 5y ago
> 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 little pet research project that's one thing, but there really needs to be ongoing workforce development, a strong advance. The NSF's TILOS, a strong alliance/nexus of researchers within & around the OpenROAD community, get this[1]: > TILOS – The Institute for Learning-enabled Optimization at Scale – is an NSF National AI Research Institute for advances in optimization, partially supported by Intel Corporation. The institute began operations in November 2021 with a mission to "make impossible optimizations possible, at scale and in practice". > There are six universities in TILOS: UCSD, MIT, National University, Penn, UT-Austin, and Yale. The institute seeks a new nexus of AI and machine learning, optimization, and use in practice. Figure 4 shows four virtuous cycles envisioned for the institute: 1. mutual advances of AI and optimization provide the foundations; 2. challenges of scale, along with breakthroughs from scaling, bind together foundations and the use domains of chip design, networks and robotics; 3. the cycle of translation and impact brings research and the leading edge of practice closer together; and 4. the cycle of research, education, and broadening participation grows the field and its workforce. The virtues written here are self evident & obvious. Trying to just get good yourself without trying to help advance the field, not participating, not taking advantages of scale of many working together, not participating in open research, the risks of having isolated teams, and not participating in cycles of development: whatever the nvidia or "publicly traded company" worlds think they're doing, they're missing out, and hurting everyone and especially themselves for this oldschool zero-sum competitive thinking. 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]. Open standards if not chips like UCI for chiplets or CXL for interconnect are again examples of literally everyone but NVidia playing well together, trying for better, standardizing a future for participation & healthy competition & growth. Nvidia again and again is the company which simply will not play with others. I challenge you to answer your own question in reverse: are any companies other than Nvidia embarking up AI/ML chipmaking in a closed fashion? There probably are, let's follow & watch them. [1] https://theopenroadproject.org/news/leveling-up-a-trajectory-of-openroad-tilos-and-beyond/ https://theopenroadproject.org/news/leveling-up-a-trajectory... [2] https://opentitan.org/ https://opentitan.org/ [3] https://github.com/chipsalliance/Cores-SweRV https://github.com/chipsalliance/Cores-SweRV

jwlake 5y ago

That article doesn't actually have enough data to really know. The anecdotes are all just design assist, make things faster from 20 mins to 3 seconds. Not singularity. There are teasers but nothing clear singularity. I think the big problem is they are just using it for fuzzy algorithm optimization, which is clearly not self learning.

rrss 5y ago

which openroad tools use "AI" rather than normal optimization like traditional EDA tools have used for decades?

areskay3 5y ago

All of the work discussed is published to some extent. References are in the slides https://arxiv.org/pdf/2012.10597.pdf https://arxiv.org/pdf/2012.10597.pdf https://research.nvidia.com/publication/2020-07_grannite-graph-neural-network-inference-transferable-power-estimation https://research.nvidia.com/publication/2020-07_grannite-gra... https://research.nvidia.com/sites/default/files/pubs/2020-07_ParaGraph:-Layout-Parasitics/057_4_Paragraph.pdf https://research.nvidia.com/sites/default/files/pubs/2020-07... https://ieeexplore.ieee.org/document/8920342 https://ieeexplore.ieee.org/document/8920342