Starter processor does AI in the water

informatics

Technological Innovation Website Editor – 10/05/2022

i processor

Ionic processor, formed by hundreds of transistors in a liquid medium.
[Imagem: Woo-Bin Jung/Harvard SEAS]

initial computation

The comparison of the brain with computers is almost inevitable, but there are fundamental differences between the two “hardware”: Processors are made of silicon and other solid-state semiconductors, while the brain processes information by manipulating ions in an aqueous medium.

This inspired Woo-Bin Jung and colleagues at Harvard University in the USA to create a single processor in a liquid solution.

Although ions in water move more slowly than electrons in semiconductors, scientists believe that the diversity of ionic species, each with its own physical and chemical properties, may offer new opportunities for richer and more diverse information processing.

Several teams have already built inion transistors, but Jung and his colleagues went much further, not only developing a complete inion circuit, made up of hundreds of chained initiating transistors, but also demonstrating that he was born cut out to perform typical artificial intelligence calculations, the so-called networks. artificial neural.

“Microprocessors digitally manipulate electrons to perform matrix multiplication,” said Professor Donhee Ham. “While our induction circuit may not be as fast or accurate as digital microprocessors, the multiplication of the electrochemical matrix in the enchanting water itself has the potential to be energy efficient.”

computer i

The processor is essentially a hardware neural network, made up of a dense array of containers that control pH at the local level.
[Imagem: Donhee Ham Research Group/Harvard SEAS]

initial neural network

The ionic transistor developed by the team consists of an aqueous solution of quinone molecules, which is in contact with two concentric ring-shaped electrodes and a central disk-shaped electrode.

By trapping and releasing hydrogen ions, the two ring electrodes electrochemically lower and adjust the local pH around the central disk. When a voltage is applied to the central disk, an electrochemical reaction is generated that produces an ionic current from the disk to the water.

The rate of reaction can be accelerated or decelerated by adjusting the local pH, causing the initiating current to increase or decrease. In other words, the pH controls the ionic current of the disk in the aqueous solution, creating an ionic counterpart of the electronic transistor – in fact, the whole system already functions as a logic gate, ready to make calculations.

Finally, the team built and chained the inonic transistors in such a way that the disk current is an arithmetic multiplication of the disk voltage and a “weight” parameter, which represents the local pH of the transistor. These transistors were arranged in a 16 x 16 matrix, expanding the analog arithmetic multiplication of the individual transistors into an analog matrix multiplication, with the matrix of local pH values ​​serving as a weight matrix such as those found in neural networks.

“Matrix multiplication is the most prevalent calculation in neural networks for artificial intelligence,” said Jung. “Our inion circuit performs the multiplication of the matrix in the water in an analogical way, totally based on electrochemical machinery.”

compute

The team plans to incorporate new types of ions to “enrich” the computation.
[Imagem: Woo-Bin Jung/Harvard SEAS]

Enrich the initial computation

The team already has plans to take early computing forward.

“So far, we’ve only used 3 or 4 ionic species, like hydrogen ions and quinone, to allow ion transport and prevent or allow the signal in the aqueous ionic transistor,” said Jung. “It will be very interesting to employ more diverse inonic species and see how we can exploit them to enrich the content of the information to be processed.”

Bibliography:

Article: An Aqueous Analog MAC Machine
Authors: Woo-Bin Jung, Han Sae Jung, Jun Wang, Henry Hinton, Maxime Fournier, Adrian Horgan, Xavier Godron, Robert Nicol, Donhee Ham
Magazine: Advanced Materials
DOI: 10.1002/adma.202205096

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