Memristors for Curious Outsiders
arxiv.org
arxiv.org
> The ideal memristor was initially and abstractly formulated as the flux–charge relationship: dΦ = M dq, (2) which, in order to be invertible, must have M defined either in terms of Φ, or q, or it must be a constant. The latter case coincides with the resistor, while if M is defined in terms of Φ ... [emphasis mine]
From my reading, this means if M is constant (like R,L,C are for "standard" components) it's just a resistor - not the miraculous "missing fourth."
Curious to get other perspectives on this.
To answer your question memristors cover the second case where the relationship isn't linear, thus tying the loose end resistors leave in the theory.
> Now you can buy one. Actually, you can buy eight in a 16-pin DIP package. It will, reportedly, cost $240 for the 16-pin DIP. That’s only $30 per memristor, and it’s the first time you can buy them.
From: https://hackaday.com/2015/07/02/new-part-day-memristors/
0: https://spectrum.ieee.org/tech-talk/semiconductors/devices/h...
> Device variability (Section 4) and volatility (Section 4.2) were mentioned as current challenges for most applications based on memristive memories. This is in contrast with biological systems, which are not built in clean rooms, and it is hard to believe that evolution exploits ideal systems in reference to its own architecture design.
> For example, in the current digital computer, computation is carried out using strings of symbols that bare no physical relationship to the quantities of interest, while, in analog computers, the latter are proportional to the physical quantities used in the computation.
Memristor functions have been implementable as active circuits for a long time and as digital logic functions as well. The real nifty thing about modern memristors is that manufacturing technologies have reached a point where we can implement passive memristance components, but they don't add anything fundamentally new to the field of machine learning except perhaps the potential of some more efficient ASICs.
Attaching machine learning to something is a good way to get published right now because it is a hype field.
We've been able to make that sort of 'learning' device with digital electronics for a while now, heck, with analog electronics too. It's just that we've lowered to component count, via memristors, to the near minimum. Memristors have the same physics as a biological synapse (really waving hands here) and the 'learning' is now down to the same part count and energy scales. Actually, the energy usage part is the most important thing. A computer takes in a lot of Watts to run something like a good chess program. But a brain takes in ~1/100th the power to do the same thing. The (likely) power reqs. of memristors are likely the most 'breakthrough' thing about them.
Or do those digital networks rely too much on very explicitly define, specific activation functions that would be difficult to tease out of memristors?
(I'd be willing to guess that it's not really an either/or situation... That much of what's being learned about digital neural nets will be useful in building analog ones, but that the messiness of electrical engineering will require some new tricks to get useful machine learning happening on the memristors.)
Memristive 'learning' is analog, by definition. Implementation of modern ML algos is a very long ways off, if they're at all useful by then. Think of it more as trying to invent a modern image classification system in 1957 when all your transistors cost $5 and are vacuum tubes. Oh, and things like a compiler aren't even dreamed of yet. It's gonna happen, but after you retire.
Specifically, you can't assume infinite impedance in the system; there are no 'bits'. You have to characterize the whole circuit around the 'memory' part and can't partition it off like you would with a normal transistor. After-all memristors kinda are a sneak circuit.
That said, there are no issues with bit length in analog designs, so, in theory, they should be continuous/infinite in their 'resolution'. Generally, memristive learning should have near infinite 'fidelity' but will likely require a lot of time to teach. Think of this type of memristive learning as a vinyl record: the sound is incredible, nearly perfect, but not very portable and hard to reproduce. Current ML stuff is more like an mp3 file: not a perfect representation of the sound, but small, cheap, and good enough.
So, in this view, trying to run analytics on the mp3 file is fine, you can use ML algos on them, plug some python at them, Bob's your uncle. But trying to do the same thing on vinyl isn't going to happen. Sure, the math is the same-ish, but the practicalities are totally different, you'd have to get an army of undergrads to just sort them and set them on physical record-players. That's the level of difference we're talking about here.
Also: I would have assumed (naively) that the memristive networks could be "faster", but now I'm realizing that this is going to be a function of how quickly the memristors transition. There are likely to be some complex timing issues in building analog neural networks that behave in remotely predictable ways. I could imagine that errors might compound in much stranger (more chaotic) ways than they would in digital networks, where "error" is pretty tame.
I imagine that the "real-time", asynchronous, and chaotic nature of analog neural networks could be seen, by the right person, in the right light, probably in the future, as a feature (not a bug). But I realize now that we're decades away from that.
My hunch is that the early practical research will be done on FPGAs with inboard memristors.