Memristor Computing On A Chip

Memristors have been — so far — mostly a solution looking for a problem. However, researchers at the University of Michigan are claiming the first memristor-based programmable computer that has the potential to make AI applications more efficient and faster.

Because memristors have a memory, they can accumulate data in a way that is common for — among other things — neural networks. The chip has both an array of nearly 6,000 memristors, a crossbar array, along with analog to digital and digital to analog converters. In fact, there are 486 DACs and 162 ADCs along with an OpenRISC processor.

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Memristor May Be Fake News

The fundamental passive components of electronics are the resistor, the capacitor, the inductor, and the oscillator, right? Actually, no, oscillators aren’t considered fundamental components because they aren’t linear. Resistors, capacitors, and inductors are also irreducible. That is, you can’t combine other passive components to model them unlike, say, a potentiometer. In the last few decades, though, we’ve heard of another fundamental component — the memristor. [Isaac Abraham] asserts, though, that the memristor isn’t a new fundamental component, but just an active device.

To support that premise [Isaac] builds a periodic table of devices showing how components map to changing voltages based on the time-varying property of charge. This shows that all the basic relationships are filled and that memristor actually covers a composition of passive components. This is similar in concept to [Strukov’s] diagram implying that a memristor is the fourth quadrant of a space defined by charge vs flux. However, using the properties of this periodic table [Isaac] argues against the fundamental nature of the memristor.

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Memristors On A Chip Solve Partial Differential Equations

We were always taught that the fundamental passive components were resistors, capacitors, and inductors. But in 1971, [Leon Chua] introduced the idea of a memristor — a sort of resistor with memory. HP created one in 2008 and since then we haven’t really had the burning need to use one. In a recent Nature article, [Mohammed Zidan] and others discuss a 32 by 32 memristor array on a chip they call a memory processing unit. This analog computer on a chip is useful for certain kinds of operations that CPUs are historically not efficient at, including solving differential equations. Other applications include matrix operations used in things like machine learning and weather prediction. The paper is behind a paywall, although the usual places to find scholarly papers will probably have it soon.

There are several key ideas for using these analog elements for high-precision computing. First, the array is set up in a passive crossbar arrangement. In addition, the memristors are quantized so that different resistance values represent different numbers. For example, a memristor element that could have 16 different resistance values would allow it to operate as a base-16 digit.

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Building Memristors For Neural Nets

Most electronic components available today are just improved versions of what was available a few years ago. Microcontrollers get faster, memories get larger, and sensors get smaller,  but we haven’t seen a truly novel component for years or even decades. There is no electronic component more interesting with more novel applications than the memristor, and now they’re available commercially from Knowm, a company that is on the bleeding edge of putting machine learning directly onto silicon.

The entire point of digital circuits is to store information as a series of ones and zeros. Memristors as well store information, but do so in a completely analog way. Each memristor changes its own resistance in response to the current going through it; ‘writing’ a positive voltage lowers the resistance, and ‘writing’ a negative voltage puts the device back into a high resistance state.

Cross section of the metal chalcogenide memristor. Source: Knowm.org
Cross section of the metal chalcogenide memristor. Source: Knowm.org

This new memristor is based on research done by [Dr. Kris Campbell] of Boise State University – the same researcher responsible for silver chalcogenide memristors we saw earlier this year. Like these earlier devices, the Knowm memristror is built using silver chalcogenide molecules. To lower the resistance of the memristor, a positive voltage ‘pulls’ silver ions into the metal chalcogenide layer. The silver ions stay in this chalcogenide layer until they are ‘pushed’ back with the application of a negative voltage. This gives the memristor it’s core functionality – being able to remember how much current has gone through it.

This technology is different from the first memristors made by HP in 2008, and has allowed Knowm to create functional memristors on silicon with a relatively high yield. Knowm is currently selling a ‘tier 3’ memristor part that only has two out of eight devices failing QC testing. A ‘tier 1’ part, with all eight memristors working, is available for $220 USD.

As for applications for this memristor, Knowm is using this technology in something they call Thermodynamic RAM, or kT-RAM. This is a small coprocessor that allows for faster machine learning than would be possible with a computer with a much more traditional architecture. This kT-RAM uses a binary tree layout with memristors serving as the links between nodes.

While it’s much too soon to say if a kT-RAM processor will be better or more efficient at performing machine learning tasks in real life, a machine learning coprocessor does have a faint echo of the machine learning silicon developed during the 80s AI renaissance. Thirty years ago, neural nets on a chip were created by a few companies around Boston, until someone realized these neural nets could be simulated on a desktop PC much more efficiently. The kT-RAM is somewhat novel and highly parallel, though, and with a new electronic component it could be just what is needed to push machine learning directly into silicon.

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Hackaday Links: July 12, 2015

Adafruit is working on a series of videos that’s basically Sesame Street for electronics. G is for Ground is out, where [Adabot] discovers pipes and lightning rods are connected to ground. Oh, the rhyming. Here’s the rest of the videos so far. We can’t wait for ‘Q is for Reactive Power’.

Think you’re good enough to build an airlock 70 cubic meters in volume that can cycle once every thirty seconds? How about building a 500 mile long steel tube with zero expansion joints across active fault lines? Can you stop a 3 ton vehicle traveling at 700 miles per hour in fifteen seconds? These are the near-impossible engineering challenges demanded of the hyperloop. The fact that no company will pay for this R&D should tell you something, but that doesn’t mean you still can’t contribute.

Calling everyone that isn’t from away. [Paul] lives near Augusta, Maine and can’t find a hackerspace. Augusta is the capital of the state, so there should be a hackerspace nearby. If you’re in the area, go leave a message on his profile.

Last week we found memristors you can buy. A few years ago, [Nyle] found them while hiking. They were crudded up shell casings, and experiments with sulfur and copper produced a memristor-like trace on a curve tracer.

Need a way to organize resistors? Use plastic bags that are the same size as trading cards.

The Arduino is too easy. It must be packaged into a format that is impossible to breadboard. It should be shaped like a banana. Open source? Don’t need that. The pins are incorrectly labelled, and will be different between manufacturing runs.

New Part Day: Memristors

For the last few years, the people in the know have been wondering about the memristor. The simplest explanation of what a memristor is comes from the name itself – it’s a memory resistor. In practice it’s a little more complex, but this basic understanding is enough to convey the fact that it’s a resistor that changes its resistance based on how much current has gone through it. The memristor was first described in the 70s by [Leon Chua], the idea sat in journals for nearly forty years, and in 2008 a working memristor was created by HP Labs.

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.

These memristors are based on a silver chalcogenide (Ge2Se3). When a circuit ‘writes’ to this memristor and applies a positive voltage, silver ion migrate to the chalcogenide, forming what the datasheet (PDF) calls dendrites. This lowers the resistance of the memristor. When a negative voltage is applied to the device, these dendrites are removed, the memristor is ‘erased’, and the memristor returns to a high-resistance state.

This silver chalcogenide memristor is different from the titanium oxide memristors developed by HP Labs that is most frequently cited when it comes to this forgotten circuit element. This work is from [Kristy Campbell] of Boise State University. She’s been working on it for more than a decade now, with IEEE publications, conference proceedings (that one’s full text), and dozens of patents.

As far as applications for memristors go, there are generally two schools of thought on that. The most interesting, in terms of current computer technology, is storage. Memristors can hold either a binary 0 or a 1 in a fraction of the space NAND Flash or old-fashioned magnetic hard drives ever will. That means greater storage density, and bigger capacity hard drives with lower power requirements. These memristors have a limit of how many times they can be cycled – ‘greater than 2000 times’ according to the datasheet. That’s nearly an order of magnitude less than MLC Flash, and something wear leveling can’t reasonably compensate for. This is a new technology, though, so that could change.

The second major expected use for memristors is neural nets. Neural nets are just a series of inputs, a few neurons, outputs, and connections between all three. These connections are weighted, and the variable resistance of memristors puts them in a unique position to emulate in hardware at the most basic level what was once done with software and custom ASICs. The trade name for these memristors – Neuro-Bit – and the company name – Bio Inspired Technologies – give you a clue at what the intended use is.

As with all new technologies, there’s always something that is inevitably created that was never imagined by the original designers. What these new applications are is at this point just speculation. Now that anyone can buy one of these neat new chips, it’s going to be interesting to see what can be made with these parts.

Memristor-based Memory Prototype By 2009

An article in EETimes suggests that we may see a memristor-based memory prototype in development as soon as 2009. The memristor is claimed by many to be the theorized fourth passive circuit element, linking the fundamental circuit variables of charge and flux. This news may not sound that exciting to most computer geeks, but this new component could usher in a new era of computer memory by forming the basis of RRAM (resistive random-access memory).

Scientists at HP labs have finally confirmed that the memristor behaves as their theories predicted. The reason that the component will work so well for memory is that the process is nonvolatile and the bits themselves will only change after the CPU tells them to. The bits in current DRAM systems slowly fade out and require a refreshment every 50 nanoseconds.

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