The future of neural network computing might be a bit bleaker than expected.
A team of physicists has successfully developed an ion circuit – a processor based on the movements of charged atoms and molecules in an aqueous solution, rather than electrons in a solid semiconductor.
Since this is closer to how the brain carries information, they say, their device could be the next step in brain-like computing.
“Ion circuits in aqueous solutions seek to use ions as charge carriers for signal processing,” write the team led by physicist Woo-Bin Jung of the Harvard John A. Paulson School of Engineering and Applied Sciences ( SEAS) in a new article.
“Here we report an aqueous ion circuit… This demonstration of the functional ion circuit capable of analog computation is a step towards more sophisticated aqueous ionics.”
An important part of signal transmission in the brain is the movement of charged molecules called ions through a liquid medium. Although the incredible processing power of the brain is extremely difficult to replicate, scientists thought a similar system could be used for computation: pushing ions through an aqueous solution.
It would be slower than conventional silicon-based computing, but it could have some interesting advantages.
For example, ions can be created from a wide range of molecules, each with different properties that could be exploited in different ways.
But first, scientists need to show that it can work.
This is what Jung and his colleagues have been working on. The first step was to design a working ion transistor, a device that switches or amplifies a signal. Their most recent advance was to combine hundreds of these transistors to work together as an ion circuit.
The transistor consists of a “bulls-eye” electrode arrangement, with a small disc-shaped electrode in the center and two concentric ring electrodes around it. This interfaces with an aqueous solution of quinone molecules.
A voltage applied to the central disk generates a current of hydrogen ions in the quinone solution. Meanwhile, the two ring electrodes modulate the pH of the solution at the gate, increasing or decreasing the ion current.
This transistor performs a physical multiplication of a “weight” parameter defined by the pair of rings synchronizing with the drive voltage, producing an ion current response.
However, neural networks rely heavily on a mathematical operation called matrix multiplication, which involves multiple multiplications.
So the team designed 16-by-16 arrays of their transistors, each capable of arithmetic multiplication, to produce an ion circuit capable of performing matrix multiplication.
“Matrix multiplication is the most common computation in neural networks for artificial intelligence,” says Jung. “Our ion circuit performs matrix multiplication in water in an analog fashion based entirely on electrochemical machinery.”
There are, of course, significant limitations to the technology. The 16 currents cannot be solved separately, meaning the operation had to be done sequentially rather than simultaneously, which significantly slowed down an already relatively slow technology.
However, its success is a step towards a more sophisticated ionic calculation: it is only by seeing the problem that one can find solutions.
The next step will be to introduce a wider range of molecules into the system to see if this allows the circuit to process more complex information.
“So far, we have used only 3-4 ion species, such as hydrogen and quinone ions, to enable ion gating and transport in the aqueous ion transistor,” says Jung.
“It will be very interesting to use more diversified ionic species and to see how we can exploit them to enrich the content of the information to be processed.”
The end goal, the team notes, is not to compete with or replace electronics with ionics, but to complement, perhaps in the form of a hybrid technology with the capabilities of both.
The research has been published in Advanced materials.
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