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Neural network sommelier

25.07.2022

Scientists at NIST have developed a new type of artificial intelligence (AI) hardware that can use less power and run faster - and it can already taste wines virtually.

As with traditional computer systems, AI has physical hardware circuits and software. Hardware usually has a large number of conventional silicon chips that consume a lot of power: for example, it takes about 190 megawatt-hours (MWh) of electricity to train one modern commercial processor.

A less energy intensive approach is to use other kinds of hardware to create neural networks. One promising device is the magnetic tunnel junction (MTJ). Devices on MTJ consume several times less energy than their traditional counterparts. MTJs are faster because they store data in the same place where calculations are performed.

The new neural network, like ordinary tasters, should train their taste. The team trained the network using 148 wines made from three types of grapes. Each virtual wine had 13 characteristics that needed to be taken into account: alcohol degree, color, alkalinity and magnesium. Each characteristic was assigned a value from 0 to 1 so that the network would take it into account and distinguish one wine from another.

Next, the AI passed a virtual test with a dataset that included 30 unknown wines. The system worked with an accuracy of 95,3%.

The authors did not have the task of creating an AI sommelier. The main conclusion is that MTJ devices can be expanded and used to create new AI systems.

The amount of energy consumed by a system depends on its components, but if you use MTJs as synapses, you can cut energy consumption in half.

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