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 <p>Researchers at the Technical University of Vienna implemented both a photosensor and a neural network in <a href="https://spectrum.ieee.org/tech-talk/computing/hardware/image-neural">one electrical circuit</a>, greatly accelerating image processing.</p><p>Machine vision today consists of a light sensor - usually a CCD circuit - and a computer with machine intelligence where the signal from the sensor is sent for processing. This method is in principle satisfactory in today's larger systems on prototype self-driving cars capable of processing over a hundred frames per second. But extending machine vision to all smaller devices requires a significant reduction in both weight and electrical power, and robots that orient themselves around humans will also need to process signals faster.</p><p>This new chip chip, which combines both image capture and coarse basic processing, is basically simple: light sensing photodiodes are also neural network nodes. Such a system was able to recognize the letters "n", "v" and "z" with some learning even before sending the signal, at a theoretical frequency of up to twenty million frames per second. At the same time, it did not require external power.</p><p>However, it should be noted that this is a very primitive device: it contains only 27 sensors and can process 3 × 3 pixel images. Before practical solutions arrive, plenty of water will flow.</p>  

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