Sounds great until
> requires an analog thermodynamic computer
Wait. What?
Perhaps a trained physicist can comment on that. Thanks.
> requires an analog thermodynamic computer
Wait. What?
Perhaps a trained physicist can comment on that. Thanks.
So it's a combination of old-school analog computation and modern GPU-based code. Takes longer in practice due to the overhead of interfacing with the hardware and waiting for the integrators to settle, but the authors are claiming that an optimized implementation could outperform a purely-digital solution, as I understand it, by accelerating convergence.
The core idea being that conventional gradient descent is a linear operation at heart, while the gradients actually being traversed are curved surfaces that have to be approximated with multiple unnecessary steps if everything is done in the digital domain.
The trouble, as everybody from Seymour Cray onward has learned the hard way, is that CMOS always wins in the end, simply because the financial power of an entire industry goes into optimizing it.
Very cool work in any event, though! Best of luck with the ongoing R&D.
https://en.m.wikipedia.org/wiki/Analog_computer
Mixed signal ASIC’s often use a mix of digital and analog blocks to get the benefits of analog. It’s especially helpful for anything that eats lots of power or to prevent that (eg mobile).
Or the spaghetti approach to finding the largest value from a list of positive values (e.g. cut dry spaghetti noodles to length for each value, bundle them together and tap the bundle on a table, the one visually sticking out the most is the largest valued element).
Can't wait for spaghetti arithmetic.
Do we have a better algo than log(n) for locating the min?
I'm thinking spaghetti max align them, lay them across an arm at the midway point, sweep the shorts that fell, and repeat until all remaining are same length.