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Conventional image sensors digitize high-resolution images at fast framerates, producing a large amount of data that needs to be transmitted off thesensor for further processing. This is challenging for perception systemsoperating on edge devices, because communication is power inefficient andinduces latency. Fueled by innovations in stacked image sensor fabrication,emerging sensor-processors offer programmability and minimal processingcapabilities directly on the sensor. We exploit these capabilities bydeveloping an efficient recurrent neural network architecture, PixelRNN, thatencodes spatio-temporal features on the sensor using purely binary operations.PixelRNN reduces the amount of data to be transmitted off the sensor by afactor of 64x compared to conventional systems while offering competitiveaccuracy for hand gesture recognition and lip reading tasks. We experimentallyvalidate PixelRNN using a prototype implementation on the SCAMP-5sensor-processor platform.

PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-optimized Perception with Neural Sensors