  High-speed machine vision is increasing its importance in both scientific and
technological applications. Neuro-inspired photonic computing is a promising
approach to speed-up machine vision processing with ultralow latency. However,
the processing rate is fundamentally limited by the low frame rate of image
sensors, typically operating at tens of hertz. Here, we propose an
image-sensor-free machine vision framework, which optically processes
real-world visual information with only a single input channel, based on a
random temporal encoding technique. This approach allows for compressive
acquisitions of visual information with a single channel at gigahertz rates,
outperforming conventional approaches, and enables its direct photonic
processing using a photonic reservoir computer in a time domain. We
experimentally demonstrate that the proposed approach is capable of high-speed
image recognition and anomaly detection, and furthermore, it can be used for
high-speed imaging. The proposed approach is multipurpose and can be extended
for a wide range of applications, including tracking, controlling, and
capturing sub-nanosecond phenomena.
