Artificial intelligence (AI) is traditionally based on software, but researchers from the Vienna University of Technology have created faster intelligent hardware. The newly developed chip is able to analyze images and provide the correct output in a matter of nanoseconds.
In today’s world, automatic image recognition is used for a variety of different applications, and certain computer programs can accurately diagnose health problems like skin cancer, navigate self-driving vehicles, and control robots. This is normally done by the evaluation of image data that is delivered by cameras, but one downside is that it is time-consuming. For example, when the number of images recorded per second is high, the large volume of data that is generated often cannot be handled.
Special 2D Material
The scientists at TU Wien decided to use a special 2D material. They developed an image sensor that can recognize certain objects through training. The chip is based on an artificial neural network, and the chip can provide data about what it is seeing within nanoseconds.
The research was presented in the scientific journal Nature.
Neural networks, which are artificial systems, can represent the nerve cells that are connected to other nerve cells within our brain. One cell can affect many others, and artificial learning on a computer works in a similar way.
“Typically, the image data is first read out pixel by pixel and then processed on the computer,” says Thomas Mueller. “We, on the other hand, integrate the neural network with its artificial intelligence directly into the hardware of the image sensor. This makes object recognition many orders of magnitude faster.”
The chip, which is based on photodetectors made of tungsten diselenide, was developed and manufactured at the TU Vienna. Tungsten diselenide is an ultra-thin material that consists of just three atomic layers. Each one of the individual photodetectors, or the “pixels” of the camera, are connected to output elements, which provides the results of object recognition.
“In our chip, we can specifically adjust the sensitivity of each individual detector element — in other words, we can control the way the signal picked up by a particular detector affects the output signal,” says Lukas Mennel, first author of the publication. “All we have to do is simply adjust a local electric field directly at the photodetector.”
They make this adaptation externally and through the use of a computer program. The sensor can be used to record different letters and adjust the sensitivities of the individual pixels. There will always be a corresponding output signal.
Neural Network Takes Over
After the completion of the learning process, the computer is not needed. The neural network is capable of operating alone, and it can produce an output signal within 50 nanoseconds.
“Our test chip is still small at the moment, but you can easily scale up the technology depending on the task you want to solve,” says Thomas Mueller. “In principle, the chip could also be trained to distinguish apples from bananas, but we see its use more in scientific experiments or other specialized applications.”
This technology will be most useful in areas that require extremely high speed, such as fracture mechanics and particle detection.
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