Glad to hear you find it exciting. I do too.. its a really hot field at the moment and I mean that in the good, not bad way ;) Lots of groups working in parallel on somewhat orthogonal design and architecture issues, with a variety of different considered devices, but a common basis set is emerging ;)
So, here's the paper I mentioned above. I think this is very methodical and inventive and definitely one of the best yet at considering confluence of DNNs and memristive (ReRAM) devices. A quick search revealed this was already on HN.
https://arxiv.org/abs/1603.07341
So, I already mentioned the iconic Strukov paper above and my own which is really quite similar to Strukov in learning strategy/philosophy, except for we used entirely chemical and 'slow' devices , which may be quite interesting for brain emulation. (remember the brain operates in the mS , or microsecond regime and not nanosecond).
Here's another article I just stumbled upon a few days ago but which looks quite promising and brings us into the territory of a more un-supervised /probabilistic algorithm for learning.
http://www.nature.com/articles/ncomms12611
So, here's the paper I mentioned above. I think this is very methodical and inventive and definitely one of the best yet at considering confluence of DNNs and memristive (ReRAM) devices. A quick search revealed this was already on HN. https://arxiv.org/abs/1603.07341
So, I already mentioned the iconic Strukov paper above and my own which is really quite similar to Strukov in learning strategy/philosophy, except for we used entirely chemical and 'slow' devices , which may be quite interesting for brain emulation. (remember the brain operates in the mS , or microsecond regime and not nanosecond).
Here's another article I just stumbled upon a few days ago but which looks quite promising and brings us into the territory of a more un-supervised /probabilistic algorithm for learning. http://www.nature.com/articles/ncomms12611