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Thermodynamic Natural Gradient Descent

arxiv.org

31–34 of 34 posts

Re: Thermodynamic Natural Gradient Descent

#31

Earlier quoted context omitted.

First author of the paper here. That's it indeed! One thing is that this is entirely CMOS-compatible. You could also do something similar with optics or other platforms, but we chose electronic circuits for this reason specifically.

Can you implement this on an FPGA?

Any good electronics engineer can. Question is just who will pay for it? Or closer to YC environment, question is monetization.

Re: Thermodynamic Natural Gradient Descent

#34

Earlier quoted context omitted.

Analog computers have a lot of history. You can Google analog with neural network or differential equations to get many results. They are fast with low power, can have precision issues, and require custom, chip design. 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 po…

Hard to beat the string algorithm for finding shortest paths on a positive weights network (e.g. build the network out of string where topologies match and link lengths are link weights, find the origin and destination nodes/knots of interest, grab the two nodes and pull until taut). Or the spaghetti approach to finding the largest value from a list of positive values (e.g. cut dry spaghetti noodles to length for eac…

Of course, we already need to be working in Spaghetti ints or prepping them will be as complex than a linear scan.

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.

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