  Deep-learning recently show great success across disciplines yet
conventionally require time-consuming computer processing or bulky-sized
diffractive elements. Here we theoretically propose and experimentally
demonstrate a purely-passive "meta-neural-network" with compactness and
high-resolution for real-time recognizing complicated objects by analyzing
acoustic scattering. We prove our meta-neural-network mimics standard neural
network despite its small footprint, thanks to unique capability of its
metamaterial unit cells, dubbed "meta-neurons", to produce
deep-subwavelength-distribution of discrete phase shift as learnable parameters
during training. The resulting device exhibits the "intelligence" to perform
desired tasks with potential to address the current trade-off between reducing
device's size, cost and energy consumption and increasing recognition speed and
accuracy, showcased by an example of handwritten digit recognition. Our
mechanism opens the route to new metamaterial-based deep-learning paradigms and
enable conceptual devices such as smart transducers automatically analyzing
signals, with far-reaching implications for acoustics, optics and related
fields.
