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Googles TPU muscle in 2017 [Prolog Community is Sleepy Joe] (Was: Zeus: A Language for Expressing Algorithms in Hardware)

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Hi,

Well I am currently looking in Local AI when I
consider NPUs, which have very small floats.
An example of bigger AI accelerators are TPUs,

which can deal with larger floats. Subsequently
then can also deal with a larger integer range.
I only read this anecdote yesterday:

"In December 2017, Stockfish 8 was used as a
benchmark to test Google division DeepMind's
AlphaZero, with Stockfish running on CPU and
AlphaZero running on Google's proprietary
Tensor Processing Units (TPUs).

AlphaZero was trained through self-play for
a total of nine hours, and reached Stockfish's
level after just four. AlphaZero also played
twelve 100-game matches against Stockfish starting
from twelve popular openings for a final score
of 290 wins, 886 draws and 24 losses, for a
point score of 733:467."
https://en.wikipedia.org/wiki/Stockfish_(chess)#Stockfish_8_versus_AlphaZero

And then:

"AlphaZero's victory over Stockfish sparked a
flurry of activity in the computer chess community,
leading to a new open-source engine aimed at
replicating AlphaZero, known as Leela Chess Zero.
The two engines remained close in strength for a
while, but Stockfish has pulled away since the
introduction of NNUE, winning every TCEC
season since Season 18."

Meanwhile the Prolog community: Sleepy Joe

LoL

Bye

Mild Shock schrieb:
> Hi,
> 
> What mindset is needed to program an NPU. Mostlikely
> a mindset based on fork/join parallelism is nonsense.
> What could be more fruitful is view the AI accellerator
> 
> as a blackbox that runs a neural network, whereby
> a neural network can be effectively viewed as a form
> of hardware, although unter the hood, it is open weights
> 
> and matrix operations. So the mindest needs:
> 
> Zeus: A Language for Expressing Algorithms in Hardware
> K. J. Lieberherr -  01 February 1985
> https://dl.acm.org/doi/10.1109/MC.1985.1662799
> 
> What changed back to then?
> 
> - 80's Field Programmable Gate Array (FPGA)
> 
> - 20's AI Boom: NPUs, Unified Memory and Routing Fabric
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> I already posted how to do SAT and Clark Completion
>> with ReLU. This was a post from 15.03.2025, 16:13,
>> see also below. But can we do CLP as well? Here
>>
>> is a take on the dif/2 constraint, or more precisely
>> a very primitive (#\=)/2 from CLP(FD), going towards
>> analogical computing. Might work for domains that
>>
>> fit into the quantization size of a NPU:
>>
>> 1) First note that we can model abs() via ReLU:
>>
>> abs(x) = ReLU(x) + ReLU(- x)
>>
>> 2) Then note that for integer values, we can model
>> chi(x>0), the characteristic function of the predicate x > 0:
>>
>> chi(x>0) = 1 - ReLU(1 - x).
>>
>> 3) Now chi(x=\=y) is simply:
>>
>> chi(x=\=y) = chi(abs(x - y) > 0)
>>
>> Now insert the formula for chi(x>0) based on ReLU
>> and the formula for abs() based on ReLU. Eh voila you
>> got an manually created neural network for the
>>
>> (#\=)/2 condition of CLP(FD), constraint logic
>> programming for finite domains.
>>
>> Have Fun!
>>
>> Bye
>>
>> Mild Shock schrieb:
>>  > A storm of symbolic differentiation libraries
>>  > was posted. But what can these Prolog code
>>  > fossils do?
>>  >
>>  > Does one of these libraries support Python symbolic
>>  > Pieceweise ? For example one can define rectified
>>  > linear unit (ReLU) with it:
>>  >
>>  >                  /   x      x  >= 0
>>  >      ReLU(x) := <
>>  >                  \   0      otherwise
>>  >
>>  > With the above one can already translate a
>>  > propositional logic program, that uses negation
>>  > as failure, into a neural network:
>>  >
>>  > NOT     \+ p             1 - x
>>  > AND     p1, ..., pn      ReLU(x1 + ... + xn - (n-1))
>>  > OR      p1; ...; pn      1 - ReLU(-x1 - .. - xn + 1)
>>  >
>>  > For clauses just use Clark Completion, it makes
>>  > the defined predicate a new neuron, dependent on
>>  > other predicate neurons,
>>  >
>>  > through a network of intermediate neurons. Because
>>  > of the constant shift in AND and OR, the neurons
>>  > will have a bias b.
>>  >
>>  > So rule based in zero order logic is a subset
>>  > of neural network.
>>  >
>>  > Python symbolic Pieceweise
>>  > 
>> https://how-to-data.org/how-to-write-a-piecewise-defined-function-in-python-using-sympy/ 
>>
>>  >
>>  >
>>  > rectified linear unit (ReLU)
>>  > https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
>>  >
>>  > Clark Completion
>>  > https://www.cs.utexas.edu/~vl/teaching/lbai/completion.pdf
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> I am spekulating an NPU could give 1000x more LIPS.
>>> For certain combinatorial search problems. It all
>>> boils down to implement this thingy:
>>>
>>> In June 2020, Stockfish introduced the efficiently
>>> updatable neural network (NNUE) approach, based
>>> on earlier work by computer shogi programmers
>>> https://en.wikipedia.org/wiki/Stockfish_%28chess%29
>>>
>>> There are varying degrees what gets updated of
>>> a neural network. But the specs of an NPU tell
>>> me very simply the following:
>>>
>>> - An NPU can make 40 TFLOPS, all my AI Laptops
>>>    from 2025 can do that right now. The brands
>>>    are Intel Ultra, AMD Ryzen and Snapdragon X,
>>>
>>>    but I guess there might be more brands around,
>>>    which can do that with a price tag less
>>>    than 1000.- USD.
>>>
>>> - SWI Prolog can make 30 MLIPS, Dogelog Player
>>>    runs similar, some Prolog systems are faster.
>>>
>>> Now thats is 10^12 versus 10^6. If some of the
>>> LIPS can be delegated to a NPU, and if we assume
>>> for example less locality or more primitive
>>>
>>> operations that require a layering. Would could assume
>>> that from the NPU 10^12 a factor of 1000 goes
>>> away. So we might still see 10'9 LIPS emerge.
>>>
>>> Now make the calculation:
>>>
>>> - Without NPU: MLIPS
>>> - With NPU: GLIPS
>>> - Ratio: 1000x times faster
>>>
>>> Have fun!
>>>
>>> Bye
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> So Boris the Loris and Nazi Retartd Julio are
>>>> not alone. There is now a mobilization of the
>>>> kind of rage against the machine,
>>>>
>>>> fighting for methods without randomness. Its
>>>> almost like  Albert Einstein ascendet from his
>>>> grave and is now preaching,
>>>>
>>>> "God does not play dice"
>>>>
>>>> So how it started:
>>>>
>>>> PIVOT was an interactive program verifier designed by
>>>> L. Peter Deutsch for his Ph.D. dissertation.
>>>> Posted here by permission of L. Peter Deutsch.
>>>> https://softwarepreservation.computerhistory.org/pivot/
>>>>
>>>> How its going:
>>>>
>>>> Formal Methods: Whence and Whither?
>>>> The text also highlights the evolving role of formal
>>>> methods amidst technological advancements, such as
>>>> AI, and explores educational and standardization issues
>>>> related to their adoption.
>>>> https://de.slideshare.net/slideshow/formal-methods-whence-and-whither-keynote/273708245 
>>>>
>>>>
>>>> Can the Don Quijotes win, and fight the AI windmills?
>>>>
>>>> LoL
>>>>
>>>> Bye
>>>>
>>>> Mild Shock schrieb:
>>>>> Hi,
>>>>>
>>>>> Boris the Loris and Julio Di Egidio the Nazi Retard,
>>>>> are going for an afterwork beer. They are still
>>>>> highly confused by Fuzzy Testing:
>>>>>
>>>>> Star Trek - The 70's Disco Generation
>>>>> https://www.youtube.com/watch?v=505zvAvnreg
>>>>>
>>>>> The favorite hangout is Spock's Logic Dancefloor,
>>>>> which is known for its sharp unfuzzy wit. They
>>>>> have  a chat with Data about Disco Math,
>>>>>
>>>>> the only Math which has no Fuzzy Logic in it.
>>>>>
>>>>> Bye
>>>>>
>>>>> Mild Shock schrieb:
>>>>>> Hi,
>>>>>>
>>>>>> Candidate Recommendation Draft - 30 September 2025
>>>>>> https://www.w3.org/TR/webnn
>>>>>>
>>>>>> WebNN samples by Ningxin Hu, Intel, Shanghai
>>>>>> https://github.com/webmachinelearning/webnn-samples
>>>>>>
>>>>>> Bye
>>>>>>
>>>>>> Mild Shock schrieb:
>>>>>>> Hi,
>>>>>>>
>>>>>>> It seems I am having problems pacing with
>>>>>>> all the new fancy toys. Wasn't able to really
>>>>>>> benchmark my NPU from a Desktop AI machine,
>>>>>>>
>>>>>>> picked the wrong driver. Need to try again.
>>>>>>> What worked was benchmarking Mobile AI machines.
>>>>>>> I just grabbed Geekbench AI and some devices:
>>>>>>>
>>>>>>> USA Fab, M4:
>>>>>>>
>>>>>>>      sANN    hANN    qANN
>>>>>>> iPad CPU    4848    7947    6353
>>>>>>> iPad GPU    9752    11383    10051
>>>>>>> iPad NPU    4873    36544    *51634*
>>>>>>>
>>>>>>> China Fab, Snapdragon:
>>>>>>>
>>>>>>>      sANN    hANN    qANN
>>>>>>> Redmi CPU    1044    950    1723
>>>>>>> Redmi GPU    480    905    737
>>>>>>> Redmi NNAPI    205    205    469
>>>>>>> Redmi QNN    226    226    *10221*
>>>>>>>
>>>>>>> Speed-Up via NPU is factor 10x. See the column
>>>>>>> qANN which means quantizised artificial neural
>>>>>>> networks, when NPU or QNN is picked.
>>>>>>>
>>>>>>> The mobile AI NPUs are optimized using
>>>>>>> mimimal amounts of energy, and minimal amounts
>>>>>>> of space squeezing (distilling) everything
>>>>>>>
>>>>>>> into INT8 and INT4.
>>>>>>>
>>>>>>> Bye
>>>>>>
>>>>>
>>>>
>>>
>>
> 

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Thread

NPUs (Neural Processing Units) are the new normal Mild Shock <janburse@fastmail.fm> - 2025-10-15 16:15 +0200
  AI most hated by formal verification (Re: Fuzzy Alert: Boris the Loris on the Dancefloor) Mild Shock <janburse@fastmail.fm> - 2025-11-26 17:03 +0100
    Googles TPU muscle in 2017 [Prolog Community is Sleepy Joe] (Was: Zeus: A Language for Expressing Algorithms in Hardware) Mild Shock <janburse@fastmail.fm> - 2025-11-27 15:22 +0100
    The tables have turned: GigaLIP on the Laptop? (Re: 100% serious Giga Logical Inferences per Second (GLIPS)) Mild Shock <janburse@fastmail.fm> - 2025-11-28 15:13 +0100

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