# Meta-neural-network for Realtime and Passive Deep-learning-based Object Recognition

arXiv: https://arxiv.org/abs/1909.07122 · HTML: https://mlsanity.com/paper/1909.07122/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Training all-mechanical neural networks for task learning through in situ backpropagation](https://mlsanity.com/paper/2404.15471/) (2024)
- [Acoustic Structure Inverse Design and Optimization Using Deep Learning](https://mlsanity.com/paper/2102.02063/) (2021)
- [Impact of Neuron Models on Spiking Neural Networks performance. A Complexity Based Classification Approach](https://mlsanity.com/paper/2509.06970/) (2025)
- [Fully Analog Resonant Recurrent Neural Network via Metacircuit](https://mlsanity.com/paper/2604.17277/) (2026)

## Abstract (arXiv)

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.