Hacker News Reader

Comment by stochastician | Hacker News Reader

(Totally self-promoting here) I did a lot of work along this line in my PhD -- stochastic architectures for probabilstic computation. http://ericjonas.com/pages/circuits.html There's an increasing amount of interest in this space.

View on HN

You're work is interesting. The concepts remind me of work in analog implementations of neural circuitry. See, the brain is likely a general-purpose, analog computer with digital-like parts. So, trying analog implementations was thought to have improvements. It did with one wafer-scale method I didn't see coming.

http://yann.lecun.com/exdb/publis/pdf/boser-92.pdf

http://www.kip.uni-heidelberg.de/Veroeffentlichungen/downloa...

It would be interesting to see someone combine the principles of your work with analog implementations on a decent process node. Yours is kind of like a hybrid between properties of analog and digital cells. The real thing might be even more effective albeit harder to automate. There's some analog EDA but it's almost always custom work.

Sounds like really neat work. It reminds me of an idea I've toyed with just a little: you know how chemical reaction networks can compute? E.g. https://johncarlosbaez.wordpress.com/2014/03/23/programming-... Last I heard, they'd used DNA strand displacement in vitro for simple neural network stuff. So I wondered how practical it'd be to encode a Bayes net as a reaction network, with the Gibbs sampling done directly by thermodynamics. This wouldn't compete with the sort of thing we do in silicon; maybe it'd work out for tiny diagnostic systems in synthetic bio? With less overhead than the neural nets.

(Well, that took more space than I wanted. I downloaded one of your circuits papers for a gander.)

Reply on news.ycombinator.com