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In Budget Laptops the TOPS come with low energy footprint (Re: π-WAM is not adding decimals, it is removing decimals)

Cross-posted to 3 groups.

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Hi,

Because of the mobile GPU design, the TOPS,
aka “Trillions of Operations Per Second.”
come with not extremly high power consumption.

Especially the presence of vector and matrix
operations can lower the energy consumption,
since they can avoid redundant memory access.

Its quite a difference between discrete graphic
cards and accelerator iGPUs that are directly
on the silicon chip, and have mobile design.

So basically with newer AI Laptops you get more
performence units for less energy units.

Have Fun!

Bye

Mild Shock schrieb:
> Hi,
> 
> Come on Horsy Boy, you can do better. I
> no where wrote something about curve
> fitting and/or increasing the precision of
> 
> float point numbers:
> 
> 11.4 Giga Lips with a Budget Laptop
> https://github.com/Jean-Luc-Picard-2021/gigabudget
> 
> What makes you think LIPS measures precision?
> You should know better as a 50% Prologer.
> 
> I explictily wrote here what the goal is:
> 
> "shave off some of the TOPS to do Prolog inferencing"
> 
> What are TOPS? Its a metric for GPUs:
> 
> TOPS stands for “Trillions of Operations Per Second.”
> https://www.lenovo.com/us/en/glossary/tops-in-computing/
> 
> See for yourself what is behind my post:
> 
> 11.4 Giga Lips with a Budget Laptop
> At the end of 2025 we acquired a couple of AI Laptops , that were still 
> cheap, since RAM prices had not yet rocketed. The intend was to tap into 
> the Copilot+ certified hardware, and shave off some of the TOPS to do 
> Prolog inferencing. Amazingly our π-WAM can churn 11.4 GIGA LIPS.
> 
> GPUs have evolved form lock-step to independent thread scheduling. This 
> made it possible to port the Hack VM variant, that forms the basis for 
> our π-WAM, to WebGPU computer shaders. Using NUM_SHADERS = 4096 we could 
> produce 11.4 Giga Lips on a Ryzen AI 7 350 w/ Radeon 860M.
> 
> See also:
> 
> Medium Article - 11.4 Giga Lips
> https://medium.com/2989/899b0d5c027b
> 
> So just get lost with your crazy irrelevant rant.
> When I get more LIPS, things run faster, and
> I remove digits from the time dimension.
> 
> Got it. Or are you too stupid?
> 
> Bye
> 
> R Kym Horsell schrieb:
>> In comp.lang.prolog Ross Finlayson <ross.a.finlayson@gmail.com> wrote:
>> ...
>>> Data centers should pay a 10000% excise on electricity,
>>> wherever it comes from, a natural regulator of inverted economies.
>>> And by ten thousand percent I really mean a ten thousand percent.
>>
>> And what would a huge surcharge do?
>> Almost always end up affecting the less powerful end of society
>> with increased costs to services the AI industry will be doing
>> more and more of over time.
>>
>> I started a little data center (exaflops.com) many years ago.
>> In those distant days people (in fact one was a prof of computer
>> science) told me you could never make money running a supercomputer.
>> LOL. :)
>>
>> I've had many years to watch the trends and a far more efficient
>> way to solve resource problems in this area is to change the
>> algorithms. There is vast room for improvement, mostly because
>> of prevailing attitudes.
>>
>> I used to do competetion data science as a sideline. Companies
>> would pay almost any price to get an extra decimal place in
>> the accuracy of their forecasting processes. But typically
>> they were trying to supercharge a system that should be scrapped
>> and re-designed from scratch. One area I'm thinking of is
>> investment. I had a customer one time -- like many times --
>> ask to improve a system that predicted the future price of
>> various stocks. The idea (for them) was to have as accurate a
>> prediction of what some stock would be worth in a week or a month's
>> time so that some moron could use the information to decide when
>> to buy or sell the thing.
>>
>> I tried to argue the efficient thing was to create a system that
>> takes the human out of the loop altogether. It doesnt provide info
>> for someone to decide whether or not to follow the advice --
>> that is just introducing more noise into the loop and probably
>> cancels any benefit of adding a couple decimal places of precision.
>> What you *should* do is make a system that is tuned to robustly
>> maximize the profit from managing a portfolio.
>>
>> Of course they wouldnt come at that. You can't suggest taking the
>> managers out of the loop. :)
>>
>> Another idea relevant to current AI methods might be to curtail
>> use of typical neural net algorithms. Many of them try to squeeze
>> the best performance of some NN during the training  phase in
>> the hope the resulting system will generalize well enough to be useful
>> on new data. But there's kind-of a law that the harder you train
>> some system to perform a task well, the less well they can subsuently
>> perform a more general version of the same thing. It's amusing when
>> you look at the graphs of NN being trained and then tested that
>> given a more general problem to solve after being trained to solve
>> similar problems very very well the poor old NN does worse that it
>> would have done if it had 0 training in the first place.
>>
>> It's not like we dont know how to improve this kind of performance.
>> Try less hard in the training phase or make it "more noisy".
>> Turns out genetic methods are just the ticket for this.
>> The training produces less over-fitting and the resulting system
>> generalizes better than it did before training and more importantly
>> it takes maybe an order of magnitude crunching to produce a good answer
>> than the usual over-fit answer.
>>
>> Anyway. Have to go and feed the cat.
>>
> 

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