In-sensor computing for machine vision

Sight is one of our most vital senses. Biologically inspired machine vision has developed rapidly in the past decade, to the point that artificial systems can'see' in the sense of gaining valuable information from images and videos1,2, although human vision remains much more efficient. Writing in Nature, Mennel et al.3 report a design for a visual system that, rather like the brain, can be trained to classify simple images in nanoseconds. Modern image sensors such as those in digital cameras are based on semiconductor (solid-state) technology and were developed in the early 1970s; they fall into two main types, known as charge-coupled devices and active-pixel sensors4. These sensors can faithfully capture visual information from the environment, but generate a lot of redundant data.