From Newsgroup: sci.physics

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 --a 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:

-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a /-a-a x-a-a-a-a-a x-a >= 0
-a-a-a-a-a ReLU(x) := <
-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a \-a-a 0-a-a-a-a-a otherwise

With the above one can already translate a
propositional logic program, that uses negation
as failure, into a neural network:

NOT-a-a-a-a \+ p-a-a-a-a-a-a-a-a-a-a-a-a 1 - x
AND-a-a-a-a p1, ..., pn-a-a-a-a-a ReLU(x1 + ... + xn - (n-1))
OR-a-a-a-a-a p1; ...; pn-a-a-a-a-a 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
-a-a from 2025 can do that right now. The brands
-a-a are Intel Ultra, AMD Ryzen and Snapdragon X,

-a-a but I guess there might be more brands around,
-a-a which can do that with a price tag less
-a-a than 1000.- USD.

- SWI Prolog can make 30 MLIPS, Dogelog Player
-a-a 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-a 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 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:

-a-a-a-a sANN-a-a-a hANN-a-a-a qANN
iPad CPU-a-a-a 4848-a-a-a 7947-a-a-a 6353
iPad GPU-a-a-a 9752-a-a-a 11383-a-a-a 10051
iPad NPU-a-a-a 4873-a-a-a 36544-a-a-a *51634*

China Fab, Snapdragon:

-a-a-a-a sANN-a-a-a hANN-a-a-a qANN
Redmi CPU-a-a-a 1044-a-a-a 950-a-a-a 1723
Redmi GPU-a-a-a 480-a-a-a 905-a-a-a 737
Redmi NNAPI-a-a-a 205-a-a-a 205-a-a-a 469
Redmi QNN-a-a-a 226-a-a-a 226-a-a-a *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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