A water-based chip has been created that could be the future of neural networks. Reedus

A water-based chip has been created that could be the future of neural networks.  Reedus

Harvard Paulson School of Engineering (USA) has announced that it is working on water-based microprocessors that could one day replace the existing small wafer architecture.

These processors will allow you to run applications not only in entertainment, but also in artificial intelligence, working with DNA.

The chips in question are still in the prototype stage, so we shouldn’t expect them to appear in the coming years, but their working principle is impressive. Processors are based on ionics, a technology that allows you to handle different types of ions in a liquid, as opposed to electrons.

The architecture is very similar to the human brain, and although this method is slower than the modern semiconductor principle, engineers believe that various ions with different physical and chemical properties can be used for rich information processing.

Imagine a gaming PC with built-in water cooling.

© Dreamtime

As confirmation of their words, scientists displayed The technology combined ionic diodes and transistors, something no other research group had done before.

Although a 16 x 16 array is nowhere near the power of the 100 billion transistors found in 12th generation Intel processors, the researchers were still able to perform calculations on a neural network. As one of the engineers explains, matrix multiplication is the most common method of computation in neural networks, and the new Ionic circuit was able to perform this multiplication.

This means that an Ionic processor can increase speed DNA synthesis Other processes, not to mention the fact that it is more energy efficient than usual. However, it’s worth considering that engineers are just beginning to experiment with ionic particles, so it’s unlikely that we’ll see water-based neural networks in the coming years. However, it is important that they can be created in principle.

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