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Energy efficiency and improved packaging are things I can readily agree with. The last thing though - “hybrid algorithms leveraging AI efficiency” sounds an aw

Energy efficiency and improved packaging are things I can readily agree with. The last thing though - “hybrid algorithms leveraging AI efficiency” sounds an awful lot like a buzzword sales pitch. This article reminds of this other one [1] posted about 1 month ago. It’s an interview with some guys that just got done building an exascale supercomputer, in which it was originally estimated to need 1000 megawatts but ultimately only needs 60. The reporter asks about zettascale and the power requirements; they wave it off and say that the big question about whether it will even be possible in the next 10 years is getting the chip lithography small enough so that you can physically build a working zettascale supercomputer. [1] https://news.ycombinator.com/item?id=34604319 https://news.ycombinator.com/item?id=34604319

unixhero 4y ago
What are hybrid algorithms supposed to be anyways? Half algorithm & half dictionary? Half algorithm & half user manual? Half algorithm & half class?
- LilBytes 4y ago
  Half SQL statements and half array.sort() methods I assume.
  - abudabi123 4y ago
    (solve (this :by strong-ai) (that :by weak-ai))
- shmerl 4y ago
  From the article it looks like half AI guessing the solution, half some static algorithm fixing the result to be better. Not sure how it's supposed to really work.
  - adgjlsfhk1 4y ago
    one variant of this is sciml approaches where you use an ODE solver wrapped around a NN. the ODE solver guarantees you get the right conversation laws which NNs don't do well and the NN is more accurate than the hand written model since it doesn't ignore the higher order effects.
- CuriousCosmic 4y ago
  Generally the concept of hybrid algorithms is: - Half "hard maths" algorithms. i.e. cominbatorics, geometry, etc. - Half "fuzzy maths" algorithms. i.e. heuristics, approximation, machine learning. The idea being to solve the parts that can be easily solved by hard maths with those hard maths so that you can reduce the problem space for when you apply the fuzzy maths to solve the rest of the problem. In other words, it's taking the problem, breaking out discrete pieces to solve with well established hard maths, using heuristics & numerical solutions to tackle the remaining known problems without "easy" analytical solutions, then using ML to fill in all the gaps and glue the whole thing together.
- ororroro 4y ago
  The article gives a concrete example in the same paragraph: "For example, AI algorithms could get close to a solution quickly and efficiently, and then the gap between the AI answer and the true solution can be filled by high-precision computing." Interestingly the example is backwards (statistical reasoning first, hard reasoning second) compared to traditional usage of "hybrid" in AI and control contexts.
  - Dylan16807 4y ago
    That's not concrete at all.
- Certhas 4y ago
  Half conventional HPC simulation that runs best on CPU, half Neural Networks that need GPUs. As proposed for example here https://www.nature.com/articles/s41586-019-0912-1 https://www.nature.com/articles/s41586-019-0912-1 and here https://www.nature.com/articles/s42256-021-00374-3 https://www.nature.com/articles/s42256-021-00374-3 > The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning. The Frontier HPC system that AMD just delivered is aimed fully at that problem. https://en.wikipedia.org/wiki/Frontier_(supercomputer) https://en.wikipedia.org/wiki/Frontier_(supercomputer)
- deleted 4y ago
  [deleted]
jayd16 4y ago
I assume it means using ML to choose between multiple algorithms instead of more traditional heuristics. That or the algorithms use a combination battery and gasoline.
evancox100 4y ago
Re. The “hybrid algorithms” bit: I was at this talk. The example she gave was a physics sim like CFD, iterating between a fast/approximate ML-based algorithm and slow/accurate classical physics algorithm, with the output of each feeding in as the starting point of the next round. But this was just an example, clearly there are lots of area you could apply a similar approach.
- greenknight 4y ago
  I think its also leaning into their new product MI300, which has 24 cpu cores with 8 compute dies. Both CPU and GPU (and memory) on a single package. Conventional processing + AI together. Hybrid approaches.
  - evancox100 4y ago
    Yes they were really crowing about MI250X adoption and touting the upcoming MI300
  - m_mueller 4y ago
    Afaik, having worked in HPC, one area where this can be employed is error bias correction of CFD models. E.g. weather models have various biases that need to be corrected for - so far this is just done with some relatively simple statistics afaik.
- PartiallyTyped 4y ago
  So differentiable programming with nns as approximators? Cool!
- Certhas 4y ago
  The main thing AMD has in their accelerators that enables this this is unified memory between CPU and GPU. Thats really interesting.
  - tankenmate 4y ago
    Don't forget that AMD also bought Xilinx
WithinReason 4y ago
“hybrid algorithms leveraging AI efficiency”: An example I came across recently: https://arxiv.org/abs/2202.11214 https://arxiv.org/abs/2202.11214 A neural network-based solution for weather forecasting: "FourCastNet is about 45,000 times faster than traditional NWP models on a node-hour basis."
JustSomeNobody 4y ago
> The last thing though - “hybrid algorithms leveraging AI efficiency” sounds an awful lot like a buzzword sales pitch. It's supposed to. Investors want to hear this, not some crap about efficiency, when the entire world is talking about AI. Edit: TBC, I care about efficiency and it's not crap, but that's likely the view of investors.
pessimizer 4y ago
> The last thing though - “hybrid algorithms leveraging AI efficiency” sounds an awful lot like a buzzword sales pitch. Oddly, to me this just sounds like efficiency gains by potentially introducing massive security holes i.e. the vector of the Meltdown/Spectres of the future. It also seems like they're trying to sell AI as some sort of secular qubit that they'll be error-correcting.
- agumonkey 4y ago
  Not a cpu designer, but I remember AMD using AI for branch prediction since long ago. Hopefully they still mention AI in that sense and not only branding.
undersuit 4y ago
AMD already uses a Perceptron for their branch prediction. I would say they're talking about support for ML speed ups in hardware but maybe the plans also include a more complex complete neural net in hardware for data prediction.

unixhero 4y ago

What are hybrid algorithms supposed to be anyways? Half algorithm & half dictionary? Half algorithm & half user manual? Half algorithm & half class?

LilBytes 4y ago
Half SQL statements and half array.sort() methods I assume.
- abudabi123 4y ago
  (solve (this :by strong-ai) (that :by weak-ai))
shmerl 4y ago
From the article it looks like half AI guessing the solution, half some static algorithm fixing the result to be better. Not sure how it's supposed to really work.
- adgjlsfhk1 4y ago
  one variant of this is sciml approaches where you use an ODE solver wrapped around a NN. the ODE solver guarantees you get the right conversation laws which NNs don't do well and the NN is more accurate than the hand written model since it doesn't ignore the higher order effects.
CuriousCosmic 4y ago
Generally the concept of hybrid algorithms is: - Half "hard maths" algorithms. i.e. cominbatorics, geometry, etc. - Half "fuzzy maths" algorithms. i.e. heuristics, approximation, machine learning. The idea being to solve the parts that can be easily solved by hard maths with those hard maths so that you can reduce the problem space for when you apply the fuzzy maths to solve the rest of the problem. In other words, it's taking the problem, breaking out discrete pieces to solve with well established hard maths, using heuristics & numerical solutions to tackle the remaining known problems without "easy" analytical solutions, then using ML to fill in all the gaps and glue the whole thing together.
ororroro 4y ago
The article gives a concrete example in the same paragraph: "For example, AI algorithms could get close to a solution quickly and efficiently, and then the gap between the AI answer and the true solution can be filled by high-precision computing." Interestingly the example is backwards (statistical reasoning first, hard reasoning second) compared to traditional usage of "hybrid" in AI and control contexts.
- Dylan16807 4y ago
  That's not concrete at all.
Certhas 4y ago
Half conventional HPC simulation that runs best on CPU, half Neural Networks that need GPUs. As proposed for example here https://www.nature.com/articles/s41586-019-0912-1 https://www.nature.com/articles/s41586-019-0912-1 and here https://www.nature.com/articles/s42256-021-00374-3 https://www.nature.com/articles/s42256-021-00374-3 > The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning. The Frontier HPC system that AMD just delivered is aimed fully at that problem. https://en.wikipedia.org/wiki/Frontier_(supercomputer) https://en.wikipedia.org/wiki/Frontier_(supercomputer)
deleted 4y ago
[deleted]

jayd16 4y ago

I assume it means using ML to choose between multiple algorithms instead of more traditional heuristics. That or the algorithms use a combination battery and gasoline.

evancox100 4y ago

Re. The “hybrid algorithms” bit: I was at this talk. The example she gave was a physics sim like CFD, iterating between a fast/approximate ML-based algorithm and slow/accurate classical physics algorithm, with the output of each feeding in as the starting point of the next round. But this was just an example, clearly there are lots of area you could apply a similar approach.

greenknight 4y ago
I think its also leaning into their new product MI300, which has 24 cpu cores with 8 compute dies. Both CPU and GPU (and memory) on a single package. Conventional processing + AI together. Hybrid approaches.
- evancox100 4y ago
  Yes they were really crowing about MI250X adoption and touting the upcoming MI300
- m_mueller 4y ago
  Afaik, having worked in HPC, one area where this can be employed is error bias correction of CFD models. E.g. weather models have various biases that need to be corrected for - so far this is just done with some relatively simple statistics afaik.
PartiallyTyped 4y ago
So differentiable programming with nns as approximators? Cool!
Certhas 4y ago
The main thing AMD has in their accelerators that enables this this is unified memory between CPU and GPU. Thats really interesting.
- tankenmate 4y ago
  Don't forget that AMD also bought Xilinx

WithinReason 4y ago

“hybrid algorithms leveraging AI efficiency”: An example I came across recently: https://arxiv.org/abs/2202.11214 https://arxiv.org/abs/2202.11214 A neural network-based solution for weather forecasting: "FourCastNet is about 45,000 times faster than traditional NWP models on a node-hour basis."

JustSomeNobody 4y ago

> The last thing though - “hybrid algorithms leveraging AI efficiency” sounds an awful lot like a buzzword sales pitch. It's supposed to. Investors want to hear this, not some crap about efficiency, when the entire world is talking about AI. Edit: TBC, I care about efficiency and it's not crap, but that's likely the view of investors.

pessimizer 4y ago

> The last thing though - “hybrid algorithms leveraging AI efficiency” sounds an awful lot like a buzzword sales pitch. Oddly, to me this just sounds like efficiency gains by potentially introducing massive security holes i.e. the vector of the Meltdown/Spectres of the future. It also seems like they're trying to sell AI as some sort of secular qubit that they'll be error-correcting.

agumonkey 4y ago
Not a cpu designer, but I remember AMD using AI for branch prediction since long ago. Hopefully they still mention AI in that sense and not only branding.

undersuit 4y ago

AMD already uses a Perceptron for their branch prediction. I would say they're talking about support for ML speed ups in hardware but maybe the plans also include a more complex complete neural net in hardware for data prediction.