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negativeonehalf

HN member
Joined
Wed, Sep 18, 2024, 2:19 AM UTC
HN karma
66
Public activity
38 items

About negativeonehalf

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Recent public activity

  1. comment
    Comment #42210197

    Meanwhile Google has already used AI for this exact stage of chip design with AlphaChip: https://deepmind.google/discover/blog/how-alphachip-transfor... And they've responded to th…

  2. comment
    Comment #42210153

    > Nature doesn't exactly have an stellar track record ensuring Google's results are verifiable ... https://retractionwatch.com/2024/05/14/nature-earns-ire-over ... They open-source…

  3. comment
    Comment #42208197

    The "whistleblower"'s admission that he "did not have evidence to support his suspicion of fraud" is pretty damning. He fails to meet a much, much lower bar than direct observation…

  4. comment
    Comment #42208034

    Update: Synopsys disavowed Markov's paper: "Regarding the CACM article that Igor Markov's comments and writings do not represent Synopsys views or opinions in any way. Synopsys is …

  5. comment
    Comment #41733834

    Unfortunately, there aren't publicly available benchmarks for modern technology node sizes, at least not that I'm aware of. Kahng compared on 45nm and 12nm chips, which are very di…

  6. comment
    Comment #41727992

    There's a lot of... passionate discussion in this thread, but we shouldn't lose sight of the big picture -- Google has used AlphaChip in multiple generations of TPU, their flagship…

  7. comment
    Comment #41727912

    Unfortunately, commercial EDA companies generally have restrictive licensing agreements that prohibit direct public comparison. Still, the fact that Google uses it for TPU is prett…

  8. comment
    Comment #41727830

    [flagged]

  9. comment
    Comment #41727807

    See this ISPD 2022 paper where the AlphaChip authors dive more into the value of pre-training (Figure 7, Figure 8): https://dl.acm.org/doi/pdf/10.1145/3505170.3511478

  10. comment
    Comment #41691321

    In the blog post, they announce MediaTek's widespread usage, the deployment in multiple generations of TPU with increasing performance each generation, Axion, etc. Chips designed w…

  11. comment
    Comment #41691228

    For a more thorough discussion on pre-training, see this ISPD 2022 paper by the AlphaChip people: https://dl.acm.org/doi/pdf/10.1145/3505170.3511478 As for external usage of the me…

  12. comment
    Comment #41689770

    You are now using multiple new accounts based on the name of one of the authors (Anna Goldie) and her husband (Gabriel). First this one ('gabegobblegoldi'), and then 'anna-gabriell…

  13. comment
    Comment #41689666

    See their ISPD 2022 paper, which goes into more detail about the value of pre-training (e.g. Figure 7): https://dl.acm.org/doi/pdf/10.1145/3505170.3511478 Sometimes training from s…

  14. comment
    Comment #41677001

    The quick-start guide in the repo that said you don't have to pre-train for the sample test case, meaning that you can validate your setup without pre-training. That does not mean …

  15. comment
    Comment #41676304

    They optimize using a fast heuristic based on wirelength, congestion, and density, but they evaluate with full P&R. It is definitely interesting that they get good timing without e…

  16. comment
    Comment #41676211

    The Nature paper describes the importance of pre-training repeatedly. The ability to learn from experience is the whole point of the method. Pre-training is just training and savin…

  17. comment
    Comment #41676027

    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 payw…

  18. comment
    Comment #41675865

    For AlphaChip, pre-training is just training. You train, and save the weights in between. This has always been supported by the Google's open-source repository. I've read Kahng's F…

  19. comment
    Comment #41675478

    Prior to AlphaChip, macro placement was done manually by human engineers in any production setting. Prior algorithmic methods especially struggled to manage congestion, resulting i…

  20. comment
    Comment #41675410

    Definitely a big part of it. Chips enable better EDA tools, which enable better chips. First it was analytic solvers and simulated annealing, now ML. Exciting times!

  21. comment
    Comment #41675380

    FD: I have been following this whole thing for a while, and know personally a number of the people involved. The AlphaChip authors address criticism in their addendum, and in a pri…

  22. comment
    Comment #41664849

    6% is just the latest one - this is a real-deal engineering task in the chip design process, that an AI can do better than a human expert, and the gap is growing with time. I'm sur…

  23. comment
    Comment #41664198

    Chips are the limiting factor for AI, and now we have AIs making chips better than human engineers. This feels like an infinite compute cheat code, or at least a way to get us very…

  24. comment
    Comment #41633027

    Wow, thank you! I've been wanting to learn about GPUs on my next flight, and this is the perfect material for that.

  25. comment
    Comment #41600725

    I am saying that we should not destroy a major source of prosperity. Targeted advertising is far more effective than untargeted because it lets you show ads to people who might hav…