Re: Well DUH ! AI People Finally Realize They Can Ditch Most Floating-Point for Big Ints

On 13/10/2024 14:23, Pancho wrote:

On 10/13/24 13:25, The Natural Philosopher wrote:

On 13/10/2024 10:15, Richard Kettlewell wrote:

"186282@ud0s4.net" <186283@ud0s4.net> writes:

https://techxplore.com/news/2024-10-integer-addition-algorithm-energy-ai.html

[...]

   The default use of floating-point really took off when
   'neural networks' became popular in the 80s. Seemed the
   ideal way to keep track of all the various weightings
   and values.
>
   But, floating-point operations use a huge amount of
   CPU/NPU power.
>
   Seems somebody finally realized that the 'extra resolution'
   of floating-point was rarely necessary and you can just
   use large integers instead. Integer math is FAST and uses
   LITTLE power .....

>
That’s situational. In this case, the paper isn’t about using large
integers, it’s about very low precision floating point representations.
They’ve just found a way to approximate floating point multiplication
without multiplying the fractional parts of the mantissas.
>

Last I heard they were going to use D to As feeding analog multipliers. And convert back to D afterwards. for a speed/ precision tradeoff.
>

 That sounds like the 1960s. I guess this idea does sound like a slide rule.

No, apparently its a new (sic!) idea.
I think that even if it does not work successfully it is great that people are thinking outside the box.
Analogue computers could offer massive parallelism for simulating complex dynamic systems.
--
There’s a mighty big difference between good, sound reasons and reasons that sound good.
Burton Hillis (William Vaughn, American columnist)