This is one of the exotic devices in DARPA's UPSIDE competition for exascale computing. This initiative seeks to find non-state (non-transistor) based approaches to computation: exploitation of nanoscale response properties of discrete components to perform some restricted, non-binary, forms of computation. Essentially, exotic ways to abuse silicon lithography to get analog computation.<p>The idea, and this can be seen on DARPA's slides (<a href="http://www.darpa.mil/workarea/downloadasset.aspx?id=2147485714" rel="nofollow">http://www.darpa.mil/workarea/downloadasset.aspx?id=21474857...</a>), is to get computation that is several orders of magnitude higher for their specialized sets of problems than what can theoretically be reached by traditional computing models even if Moore's law continues.<p>DARPA would like to first apply this technology to ARGUS drone systems (<a href="https://www.youtube.com/watch?v=QGxNyaXfJsA" rel="nofollow">https://www.youtube.com/watch?v=QGxNyaXfJsA</a>) and related technology because streaming video can't be done to the ground, tracking and decision making must be done on board - yet traditional processing platforms can only track a few orders of magnitude fewer targets that what the military would like.<p>In a more advanced phase, if memristor or coupled oscillator (etc) approaches to building inference models become possible, then programs written in DARPA's other initiative (Probablistic Programming) could be programmed into these exotic solid state devices to compute in a way more analogous to today's generic computation. And indeed, eventually the adoption of Probablistic Programming will train programmers to write code for quantum computers - while more complicated, replacing Probablistic Programming's PDFs with probability amplitudes almost get one there.<p>I hope to see more journalistic coverage of some of the other exotic devices.
I'm very excited about this! Is there any possibility for me to work in a company working on similar stuff? That would make a dream come true for me! I live in Germany and would love to write my masters thesis a related topic!<p>This is so amazing! Btw. I have found an HP Invent sign in my town, but the security guard didn't answer any questions about it. The only thing he said was that I won't find any address or telephone number for it. That made me curious, because HP is working on a memristor based Computer, but I doubt that they produce it in Germany.
A neural network chip semi-conductor startup: <a href="http://brainchipinc.com/technology/" rel="nofollow">http://brainchipinc.com/technology/</a><p>They "backdoor listed" on to an Australian mining company, share price went from 1 cent to 27 cents:
<a href="https://www.google.com/finance?cid=11163357" rel="nofollow">https://www.google.com/finance?cid=11163357</a><p>Valued at $57m.
Memristor crossbars are exciting even outside of neural network applications: it can be used as a very dense, non-volatile memory. If this design can be scaled up, it could potentially replace both flash memory storage, and RAM.
I have nothing to say but that this is a cool application of memristors. My graduate research is in cognitive computing and the thought of using circuitry to represent the synaptic weights as opposed to hardware/software based adders and multipliers is pretty awesome.
The general theory of memristors, meminductors, memcapacitors of any order (first order memristor is the genuine one invented in 1971 by Professor Leon Chua, however he generalized recently his discover to second, third order memristor, etc.)is published in a paper I wrote with him on september 2014 in International Journal of Bifurcation and Chaos. One can download it freely from the site
<a href="https://www.researchgate.net/publication/261676241_MEMFRACTANCE_A_MATHEMATICAL_PARADIGM_FOR_CIRCUIT_ELEMENTS_WITH_MEMORY" rel="nofollow">https://www.researchgate.net/publication/261676241_MEMFRACTA...</a>
This looks cool but I'm somewhat skeptical. I would be more interested in seeing what problem the system solves better or decently (say even MNIST) rather than how it was built using memristors.<p>There is a lesson from IBM trying to mimic a rat's brain -- that is you try to solve a problem rather than just burn power.
The technology sounds very promising but if the goal is to simulate the brain, the ANN models we have today are inadequate. Current evidence suggests that it needs to incorporate dendritic dynamics and , soon, molecular computation.
Memristers are supposed to be the main memory of HP's future computing project called The Machine. It is supposed to be as fast as register memory and compact as flash.
"Even on a 30 nm process, it would be possible to place 25 million cells in a square centimeter, with 10,000 synapses on each cell. And all that would dissipate about a Watt."<p>Wow - seems like a lot.<p>Human brain by comparison (sourced by google):
- 12 watts
- 100 billion neurons
- 1000 trillion connections<p>Computing with memsisters is going to be very interesting.