My first question would be whether it's possible to influence the output via triggering power fluctuations on the motherboard - e.g. by running expensive code to cause the CPU/GPU to scale up.
Probably not. It's hard to guess, but they probably get a Poison Distribution https://en.wikipedia.org/wiki/Poisson_distribution in the detector, they may read only a few of the lower bits of the data, and then mix them in the entropy pool, with other sources. So the end result is quite unpredictable.
It's somehow similar to a random generator where you have 5 dices, roll them and then add to the entropy pool only if the total was even or odd. Changing the power is like forcing the system to use only 4 dices. It changes the probabilities a little, but not in a very controlable way, and with a good mixing in the entropy pool it's almost irrelevant.
Note if you look at the paper, you notice a close but not entirely perfect normal distribution, but nothing you cannot fix with UDNs and Irwin-Hall. For reference how that is done you can read the bottom of this very useful RNG article:
https://people.ece.cornell.edu/land/courses/ece4760/RP2040/C...
My overall verdict on the tech in OP is that it is amazingly promising!
I usually stick to lava lamps
Lava lamps have been deprecated, Lava LEDs are the new standard
Fender amps here
Only useful for random numbers up to 11 though.
My first question would be whether it's possible to influence the output via triggering power fluctuations on the motherboard - e.g. by running expensive code to cause the CPU/GPU to scale up.
Probably not. It's hard to guess, but they probably get a Poison Distribution https://en.wikipedia.org/wiki/Poisson_distribution in the detector, they may read only a few of the lower bits of the data, and then mix them in the entropy pool, with other sources. So the end result is quite unpredictable.
It's somehow similar to a random generator where you have 5 dices, roll them and then add to the entropy pool only if the total was even or odd. Changing the power is like forcing the system to use only 4 dices. It changes the probabilities a little, but not in a very controlable way, and with a good mixing in the entropy pool it's almost irrelevant.
I read the actual open access paper: https://opg.optica.org/oe/viewmedia.cfm?uri=oe-33-11-22154&s...
Note if you look at the paper, you notice a close but not entirely perfect normal distribution, but nothing you cannot fix with UDNs and Irwin-Hall. For reference how that is done you can read the bottom of this very useful RNG article: https://people.ece.cornell.edu/land/courses/ece4760/RP2040/C...
My overall verdict on the tech in OP is that it is amazingly promising!
Anybody have input on why this isn't a "Paper Tiger"?
Why would it be?
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