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IBM Creates World's First Artificial Phase-Change Neurons (arstechnica.com)
An anonymous reader writes from a report via Ars Technica: IBM has created the world's first artificial nanoscale stochastic phase-change neurons and has already created and used a population of 500 of them to process a signal in a similar manner as the brain. Ars Technica reports: "Like a biological neuron, IBM's artificial neuron has inputs (dendrites), a neuronal membrane (lipid bilayer) around the spike generator (soma, nucleus), and an output (axon). There's also a back-propagation link from the spike generator back to the inputs, to reinforce the strength of some input spikes. The key difference is in the neuronal membrane. In IBM's neuron, the membrane is replaced with a small square of germanium-antimony-tellurium (GeSbTe or GST). GST, which happens to be the main active ingredient in rewritable optical discs, is a phase-change material. This means it can happily exist in two different phases (in this case crystalline and amorphous), and easily switch between the two, usually by applying heat (by way of laser or electricity). A phase-change material has very different physical properties depending on which phase it's in: in the case of GST, its amorphous phase is an electrical insulator, while the crystalline phase conducts. With the artificial neurons, the square of GST begins life in its amorphous phase. Then, as spikes arrive from the inputs, the GST slowly begins to crystallize. Eventually, the GST crystallizes enough that it becomes conductive -- and voila, electricity flows across the membrane and creates a spike. After an arbitrary refractory period (a resting period where something isn't responsive to stimuli), the GST is reset back to its amorphous phase and the process begins again." The research has been published via the journal Nature.
IBM made a video a while ago which was a pretty interesting watch if you're interested in this stuff:
"From BrainScales to Human Brain Project: Neuromorphic Computing Coming of Age"
https://www.youtube.com/watch?v=g-ybKtY1quU
IBM actually put out quite a lot of interesting tech related videos :)
https://www.youtube.com/user/IBMLabs
So, why can't we just query them for their contents?
(I'm an AI researcher by day.)
It's a very good idea, and something that many researchers have thought about. The problem is that it's very difficult to do, and so far no one's been able to figure out a good way to do it.
The cerebral cortex is composed of "columns", where each column is about the thickness of a human hair. If you could peel the cortex off and lay it flat, it would be about as thick as a business card. To all appearances, the cortex is composed of identical columns, with some slight variations for I/O columns and such.
Each column contains roughly 100 neurons in a handful of types. Any individual neuron makes between 2000 and 15000 connections with other neurons, and some neurons in a column make connections with neurons in other columns.
So you have 100 cells in a column the thickness of a human hair and length which is the thickness of a business card.
It's difficult to make a wire thin enough to contact one neuron, it's impossible to manoeuvre such a wire to get it in place to touch one neuron, it has to have insulation everywhere except the tip to avoid signals from other neurons, and the very faint signals have to be amplified close to the source to avoid noise.
It's just about impossible to map the connections between neurons because there are so many of them and the connections are much *much* smaller than the nerves themselves. Also, you have to do this without killing the nerve, and killing other nerves you have to go through to get the connections.
And this has to be done while the organism is living, and keeping it living while drilling into the head cavity is a trick in itself (and dealing with the resulting pain, blood loss, &c.). You can get some information from non-mammals (such as sea worms), but then none of those have a mammalian cortex to study.
Every once in awhile I read about new techniques using fiber optics and related technologies, but there's still the issue of routing the sensor (whatever it may be) to the neurons in a way that doesn't chop through other nerves.
One technology I read about has a pad with tiny needles laid down on the cortex. The needles can be made using chip fabrication technology, and you can have amplifiers on the chip at the base of the needles... but this still can only be applied to the *surface* of the cortex, and only connects to those nerves which are physically at the top of the column, and not the ones inside.
All in all, it's an extremely difficult problem that no one's figured out yet.
IBM doesn't have their own fabs anymore, and even when the did the nodes they supported weren't exactly bleeding edge. Re-purposing a fab built for an outdated node to research is probably quite a bit cheaper than retooling for a smaller node, and likely gets them some nice research-friendly tax credits to boot (not to mention research grants). On top of all of that you are introducing phase change materials into a silicon fab which have previously only been used commercially in spinning optical media. Why on earth would you pile on the complexity of dealing with a bleeding edge node size on top of all the other challenges a project like this presents? Also 500 of these pseudo-neurons probably fit into a single tile, which is important when you consider most design flows don't let you do full blown simulation of an entire chip, rather you break the design down into tiles sized according to what your design flow can effective support without your hair turning gray waiting on simulation results.
Well said, sir! Also, simulated neural networks in software have been around since rather far back in the last century (on paper, back to 1933 and Nicholas Rashevsky), and as the author of a very large, very advanced neural network modeling tool, I'd further add that we can already build rather large simulated neural networks in software, and even build composite NNs using a mix of software and parallel hardware.
A transistor-based gate is already in some sense a neuron, and it isn't that difficult to build collections of them that perform even more like a neuron. The problem is that even if we do so, we don't really have any good idea what to do with it, and we have a very hard time scaling it up to the number of connections visible in the human brain. By "very hard", I mean "not possible to achieve, not likely to become possible to achieve, any time soon", at least not without a serious breakthrough. We are two orders of magnitude short of matching the number of neurons in the human brain in JUST transistor count, and we cannot come anywhere near 1000 to 10000 connections per transistor. And finally, transistors are not neurons, and even if they were neurons we have no idea how to build a massive, amorphous neural network and then train it somehow (or program it somehow) to do useful work.
It is enormously difficult to write a good simulated neural network program to do relatively simple tasks such as noisy pattern identification or predictive modeling of unstructured high dimensional data, even with complete control over the algorithm. There seems to be this feeling out there that if one just builds an artificial brain with a lot of artificial neurons and hit it with data it will somehow "wake up" and smell the metaphorical coffee of life and do some sort of useful work. I personally think this is enormously optimistic, but then, I actually have some grasp of the mathematical complexity of the optimization problem involved.
This is more in the category of building (or rebuilding with more modern technology) a unit that MAY prove useful if we ever have a breakthrough on the half-dozen serious obstacles associated with AI via NNs, most of which can actually be made and will actually be made (if at all) with simulated NNs. Only after simulated NNs demonstrate a clear pathway to going from a collection of artificial "neurons" with some specific algorithmic functionality and ability to be interconnected at a very fine scale to a useful, profitable, neural network that does actual work worth doing will anybody bother to dump a billion or so dollars into a foundry for artificial neuron devices. And there may even be a few such applications today -- some networks are very simple algorithmically, but they are also the least extendable to really hard problems or problems we cannot already solve efficiently other ways. Letter recognition, maybe.
The human brain has a quadrillion or so synaptic connections, and it is difficult to even start estimate the volume of the phase space represented by all of those connections. The "switches" are indeed much slower than they are in computers, but they run in parallel as well as serially, and it is estimated that they are "equivalent" to a terabit per second processor in their full-parallel speed. We can achieve similar scales in simulation on parallel supercomputers, of course, but not with anywhere near the number of "neurons" or "synapses" and if we really use TIPS scale computing resources, they probably aren't going to be doing their "AI" with NNs anyway for anything but selected problems.
So cool top article, good on IBM, and all that, but I'm not holding my breath for a phone that actually completes words sanely using its IBM(tm) Neural Processor...
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Even when the experts all agree, they may well be mistaken. --- Bertrand Russell.