101–110 of 215 posts
I'm pretty sure Cadence and Synopsys have both released reinforcement-learning-based placing and floor planning tools. How do they compare...?
They don't. You cannot compare reality (Cadence, Synopsys) with hype (Google).
Earlier quoted context omitted.
The original paper reports P&R metrics (WNS, TNS, area, power, wirelength, horizontal congestion, vertical congestion) - https://www.nature.com/articles/s41586-021-03544-w (no paywall): https://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2021_2022/...
From what I saw in the rebuttal papers, the Google cost-function is wirelength based. You can still get good TNS from that if your timing is very simplistic -- or if you choose your benchmark carefully.
Earlier quoted context omitted.
why do you think that?
Far more people / companies are designing PCBs than there are designing custom chips.
Earlier quoted context omitted.
The problem is that the competition (our current von neumann architecture) has billions of dollars of R&D per year invested. Better architectures without the yearly investment train will no longer be better quite quickly. You would need to be 100x to 1000x better in order to pull the investment train onto your tracks. Don’t has been impossible for decades. Even so, I think we will see such a change in my lifetime. AI…
I think it's just ignorance and timidity on the part of investors. Memristor or memory-computing startups are surely the next trend in investing within a few years. I don't think it's necessarily demand or any particular calculation that makes things happen. I think people including investors are just herd animals. They aren't enthusiastic until they see the herd moving and then they want in.
Earlier quoted context omitted.
I think it's just ignorance and timidity on the part of investors. Memristor or memory-computing startups are surely the next trend in investing within a few years. I don't think it's necessarily demand or any particular calculation that makes things happen. I think people including investors are just herd animals. They aren't enthusiastic until they see the herd moving and then they want in.
I don't think it's ignorant to not invest in something that has a decade long path towards even having a market, much less a large market.
This work from Google (original Nature paper: https://www.nature.com/articles/s41586-021-03544-w ) has been credibly criticized by several researchers in the EDA CAD discipline. These papers are of interest: - A rebuttal by a researcher within Google who wrote this at the same time as the "AlphaChip" work was going on ("Stronger Baselines for Evaluating Deep Reinforcement Learning in Chip Placement"): http://47.190.8…
It seems like this is multiple parties pursuing distinct arguments. Is Google saying that this technique is applicable in the way that the rebuttals are saying it is not? When I read the paper and the update I did not feel as though Google claimed that it is general, that you can just rip it off and run it and get a win. They trained it to make TPUs, then they used it to make TPUs. The fact that it doesn't optimize w…
Some interesting context on this work: 2 researchers were bullied to the point of leaving Google for Anthropic by a senior researcher (who has now been terminated himself): https://www.wired.com/story/google-brain-ai-researcher-fired... They must feel vindicated by their work turning out to be so fruitful now.
Earlier quoted context omitted.
The original paper reports P&R metrics (WNS, TNS, area, power, wirelength, horizontal congestion, vertical congestion) - https://www.nature.com/articles/s41586-021-03544-w (no paywall): https://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2021_2022/...
From what I saw in the rebuttal papers, the Google cost-function is wirelength based. You can still get good TNS from that if your timing is very simplistic -- or if you choose your benchmark carefully.
Some interesting context on this work: 2 researchers were bullied to the point of leaving Google for Anthropic by a senior researcher (who has now been terminated himself): https://www.wired.com/story/google-brain-ai-researcher-fired... They must feel vindicated by their work turning out to be so fruitful now.
[1] https://www.theregister.com/AMP/2023/03/27/google_ai_chip_pa...
[2] https://regmedia.co.uk/2023/03/26/satrajit_vs_google.pdf
I must be old because first thing I thought reading AlphaChip was why is deepmind talking about chips in DEC Alpha :-) https://en.wikipedia.org/wiki/DEC_Alpha .