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| Started by | Mild Shock <janburse@fastmail.fm> |
|---|---|
| First post | 2025-10-15 16:15 +0200 |
| Last post | 2025-11-28 15:13 +0100 |
| Articles | 4 — 1 participant |
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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
| From | Mild Shock <janburse@fastmail.fm> |
|---|---|
| Date | 2025-10-15 16:15 +0200 |
| Subject | NPUs (Neural Processing Units) are the new normal |
| Message-ID | <10coa9b$12ucf$6@solani.org> |
Hi,
It seems I am having problems pacing with
all the new fancy toys. Wasn't able to really
benchmark my NPU from a Desktop AI machine,
picked the wrong driver. Need to try again.
What worked was benchmarking Mobile AI machines.
I just grabbed Geekbench AI and some devices:
USA Fab, M4:
sANN hANN qANN
iPad CPU 4848 7947 6353
iPad GPU 9752 11383 10051
iPad NPU 4873 36544 *51634*
China Fab, Snapdragon:
sANN hANN qANN
Redmi CPU 1044 950 1723
Redmi GPU 480 905 737
Redmi NNAPI 205 205 469
Redmi QNN 226 226 *10221*
Speed-Up via NPU is factor 10x. See the column
qANN which means quantizised artificial neural
networks, when NPU or QNN is picked.
The mobile AI NPUs are optimized using
mimimal amounts of energy, and minimal amounts
of space squeezing (distilling) everything
into INT8 and INT4.
Bye
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| From | Mild Shock <janburse@fastmail.fm> |
|---|---|
| Date | 2025-11-26 17:03 +0100 |
| Subject | AI most hated by formal verification (Re: Fuzzy Alert: Boris the Loris on the Dancefloor) |
| Message-ID | <10g78cv$l82g$2@solani.org> |
| In reply to | #894449 |
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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| From | Mild Shock <janburse@fastmail.fm> |
|---|---|
| Date | 2025-11-27 15:22 +0100 |
| Subject | Googles TPU muscle in 2017 [Prolog Community is Sleepy Joe] (Was: Zeus: A Language for Expressing Algorithms in Hardware) |
| Message-ID | <10g9mr8$oiad$1@solani.org> |
| In reply to | #894568 |
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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| From | Mild Shock <janburse@fastmail.fm> |
|---|---|
| Date | 2025-11-28 15:13 +0100 |
| Subject | The tables have turned: GigaLIP on the Laptop? (Re: 100% serious Giga Logical Inferences per Second (GLIPS)) |
| Message-ID | <10gcam8$ofi3$3@solani.org> |
| In reply to | #894568 |
Hi, Notably we try to do something with AI laptops that came out in 2025. Tapping into their NPU. Back in the late 80's GigaLIP were rather hypothetical, small machines could hardly do KLIPs. Today small machines do easily MLIPs. But hardly any popular Prolog systems already taps into the AI Boom, they are all Sleepy Joes. Not to mention populate by morons like Boris the Loris and Nazi Retard Julio. They simply cannot connect the dots. Bye P.S.: Nice travel to the past is this paper: Is a GigaLIP Fast Enough? TOM W. KELLER - December 1988 DOI: 10.1007/BF00436711 Mild Shock schrieb: > > Hi, > > I am 100% serious about Giga Logical Inferences > per Second (GLIPS). Leaving behind the sequential > constraint solving world: > > The Complexity of Constraint Satisfaction Revisited > https://www.cs.ubc.ca/~mack/Publications/AIP93.pdf > > Only I have missed the deep learning bandwagon, > never programmed with PyTorch or Keras. So even > for the banal problem of coding some > > ReLU networks and shipping them to a GPU or NPU, > or a hybrid, I don't have much experience. So > I am marveling at papers such as: > > Learning Variable Ordering Heuristics > for Solving Constraint Satisfaction Problems > https://arxiv.org/abs/1912.10762 > > Given that the AI Boom started after 2019, > the above paper is already old, and it has > currious antique terminology like Multilayer > > Perceptron, which is not so common anymore? > It does also more than what I want to demonstrate, > it does also do policy learning. > > Bye > > 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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