  As conventional frame-based cameras suffer from high energy consumption and
latency, several new types of image sensors have been devised, with some of
them exploiting the sparsity of natural images in some transform domains.
Instead of sampling the full image, those devices capture only the coefficients
of the most relevant spatial frequencies. The number of samples can be even
sparser if a signal only needs to be classified rather than being fully
reconstructed. Based on this mathematical framework, we developed an image
sensor that can be trained to classify optically projected images by reading
out the few most relevant pixels. The device is based on a two-dimensional
array of metal-semiconductor-metal photodetectors with individually tunable
photoresponsivity values. We demonstrate its use for the classification of
handwritten digits with an accuracy comparable to that of conventional systems,
but with lower delay and energy consumption.
