# RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement

arXiv: https://arxiv.org/abs/2207.06883 · HTML: https://mlsanity.com/paper/2207.06883/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [Neuromorphic Silicon Photonic Networks](https://mlsanity.com/paper/1611.02272/) (2016)
- [An optical neural network using less than 1 photon per multiplication](https://mlsanity.com/paper/2104.13467/) (2021)
- [Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)](https://mlsanity.com/paper/1907.01525/) (2019)
- [Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks](https://mlsanity.com/paper/1903.04579/) (2019)
- [Metasurface-Enabled On-Chip Multiplexed Diffractive Neural Networks in the Visible](https://mlsanity.com/paper/2107.07873/) (2021)
- [Scale MLPerf-0.6 models on Google TPU-v3 Pods](https://mlsanity.com/paper/1909.09756/) (2019)
- [Single-Shot Optical Neural Network](https://mlsanity.com/paper/2205.09103/) (2022)
- [Machine Learning Approach to RF Transmitter Identification](https://mlsanity.com/paper/1711.01559/) (2017)
- [Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN](https://mlsanity.com/paper/2101.01668/) (2020)
- [Vision, Requirements, and Technology Trend of 6G: How to Tackle the Challenges of System Coverage, Capacity,User Data-Rate and Movement Speed](https://mlsanity.com/paper/2002.04929/) (2020)
- [All-Photonic Artificial Neural Network Processor Via Non-linear Optics](https://mlsanity.com/paper/2205.08608/) (2022)

## Built on by (most cited first, 2 of 2)

- [Quantum-limited stochastic optical neural networks operating at a few quanta per activation](https://mlsanity.com/paper/2307.15712/) (2023)
- [Machine Intelligence on Wireless Edge Networks](https://mlsanity.com/paper/2506.12210/) (2025)

## Abstract (arXiv)

Edholm's Law predicts exponential growth in data rate and spectrum bandwidth for communications and is forecasted to remain true for the upcoming deployment of 6G. Compounding this issue is the exponentially increasing demand for deep neural network (DNN) compute, including DNNs for signal processing. However, the slowing of Moore's Law due to the limitations of transistor-based electronics means that completely new paradigms for computing will be required to meet these increasing demands for advanced communications. Optical neural networks (ONNs) are promising DNN accelerators with ultra-low latency and energy consumption. Yet state-of-the-art ONNs struggle with scalability and implementing linear with in-line nonlinear operations. Here we introduce our multiplicative analog frequency transform ONN (MAFT-ONN) that encodes the data in the frequency domain, achieves matrix-vector products in a single shot using photoelectric multiplication, and uses a single electro-optic modulator for the nonlinear activation of all neurons in each layer. We experimentally demonstrate the first hardware accelerator that computes fully-analog deep learning on raw RF signals, performing single-shot modulation classification with 85% accuracy, where a 'majority vote' multi-measurement scheme can boost the accuracy to 95% within 5 consecutive measurements. In addition, we demonstrate frequency-domain finite impulse response (FIR) linear-time-invariant (LTI) operations, enabling a powerful combination of traditional and AI signal processing. We also demonstrate the scalability of our architecture by computing nearly 4 million fully-analog multiplies-and-accumulates for MNIST digit classification. Our latency estimation model shows that due to the Shannon capacity-limited analog data movement, MAFT-ONN is hundreds of times faster than traditional RF receivers operating at their theoretical peak performance.