Re: GETF Speedup?

> On 16 Oct 2025, at 23:42, Bradford Miller <[email protected]> wrote:
> 
> Back to GETF: all software, but the Symbolics approach I previously referenced is related to the "self-reconfiguration depending on your data", problem.

This looks like an interesting undertaking, which is essentially designing cyborgs, or to be precise, mechanisms that can adapt to their environment, and rewire themselves to it: much like we do in various settings, at work we behave a certain way, with our families we behave in another way, that would look very weird in a work setting. I understand it, the main drive for it is essentially a search for performance — we want to use the most out of our computing hardware, so we can reconfigure it to be better at certain tasks when needed.

However, per se it is not different to a conventional machine that runs different code branches depending on the inputs, in this way adapting to the problem, like OpenSSL can produce a pair of private/public keys, or encrypting a block of data depending on the parameters it was called with. Only in our cyborg case we not only choose a different part of code to run, we also modify the hardware to run this part of code on more efficiently. It follows that there is nothing special about hardware morphing — if we had an infinitely fast ARM64 processor, we could just run whatever code we need in a given situation, even though if it would be more “verbose” than may be needed in theory.

We are not overcoming drawbacks of von Neumann’s architecture per se, but the limitations of our current computational hardware implementations. The problem becomes, does the cost of hardware morphing outweigh overhead costs of running code “redundant” for this particular configuration.

This approach is not wrong, of course, given that our current hardware does have certain limitation (CPU vs RAM speed disparity, that is), however the main challenge that I am seeing is not in morphing hardware per se, but the choice of input parameters that decide what configuration to be used. It does not appear to be possible to do it fully automatically: a system cannot do anything it wasn’t programmed to, someone has to look into it at the design stage and say “when the input combination from sensors is similar to this, switch into this mode, or when the set of inputs is this, use that mode”.

That means that behind all this T-1000 morphing machinery will be a single point of control (possibly running on a raspberry pi), that monitors the sensor inputs and decides which branch of code to take, so to speak, and morph accordingly. It appears that morphing hardware is an optimisation facade behind something that essentially remains a code branch selector and their follower based on specific rules and conditions. It does not matter how these rules were created, by hand, or by aggregated data analysis (machine learning, that is), it will still remain a more or less static algorithm.

This is not to take away from your work, which I do find very curious as just recently I was reading about how FPGAs work, but the question arises: do you think that the morphing hardware concept is more or less a hack that won’t be needed if suddenly the speed of common computational hardware suddenly stops being a limiting factor (say, atom based transistors, or something, magic hyperspace RAM access protocols maybe)? 

Does you research show that such adaptable systems will inevitably boil down to a set of if-then-else constructs tailored to respond to a preselected set of input parameters (note that machine learning does not change anything here in principle, we just have a bigger selection of input combinations we can respond to), or do you have reasons to believe that such systems have a potential to respond adequately to genuinely novel situations?