# Single-Shot Optical Neural Network

arXiv: https://arxiv.org/abs/2205.09103 · HTML: https://mlsanity.com/paper/2205.09103/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 99 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Neuromorphic Silicon Photonic Networks](https://mlsanity.com/paper/1611.02272/) (2016)
- [Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit](https://mlsanity.com/paper/2008.11659/) (2020)
- [An optical neural network using less than 1 photon per multiplication](https://mlsanity.com/paper/2104.13467/) (2021)
- [Rethinking floating point for deep learning](https://mlsanity.com/paper/1811.01721/) (2018)
- [Massively Parallel Amplitude-Only Fourier Neural Network](https://mlsanity.com/paper/2008.05853/) (2020)
- [Enabling real-time multi-messenger astrophysics discoveries with deep learning](https://mlsanity.com/paper/1911.11779/) (2019)
- [Hybrid training of optical neural networks](https://mlsanity.com/paper/2203.11207/) (2022)
- [Meta-optic Accelerators for Object Classifiers](https://mlsanity.com/paper/2201.11034/) (2022)

## Built on by (most cited first, 12 of 12)

- [Image sensing with multilayer, nonlinear optical neural networks](https://mlsanity.com/paper/2207.14293/) (2022)
- [Quantum-limited stochastic optical neural networks operating at a few quanta per activation](https://mlsanity.com/paper/2307.15712/) (2023)
- [Transferable polychromatic optical encoder for neural networks](https://mlsanity.com/paper/2411.02697/) (2024)
- [RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement](https://mlsanity.com/paper/2207.06883/) (2022)
- [Quantum-secure multiparty deep learning](https://mlsanity.com/paper/2408.05629/) (2024)
- [Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision](https://mlsanity.com/paper/2306.07365/) (2023)
- [Hyperspectral In-Memory Computing with Optical Frequency Combs and Programmable Optical Memories](https://mlsanity.com/paper/2310.11014/) (2023)
- [Compute-first optical detection for noise-resilient visual perception](https://mlsanity.com/paper/2403.09612/) (2024)
- [Metasurface-generated large and arbitrary analog convolution kernels for accelerated machine vision](https://mlsanity.com/paper/2409.18614/) (2024)
- [Machine Intelligence on Wireless Edge Networks](https://mlsanity.com/paper/2506.12210/) (2025)
- [Direct Kernel Optimization: Efficient Design for Opto-Electronic Convolutional Neural Networks](https://mlsanity.com/paper/2511.02065/) (2025)
- [Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks](https://mlsanity.com/paper/2601.22300/) (2026)

## Abstract (arXiv)

As deep neural networks (DNNs) grow to solve increasingly complex problems, they are becoming limited by the latency and power consumption of existing digital processors. For improved speed and energy efficiency, specialized analog optical and electronic hardware has been proposed, however, with limited scalability (input vector length K of hundreds of elements). Here, we present a scalable, single-shot-per-layer analog optical processor that uses free-space optics to reconfigurably distribute an input vector and integrated optoelectronics for static, updatable weighting and the nonlinearity -- with K ≈ 1,000 and beyond. We experimentally test classification accuracy of the MNIST handwritten digit dataset, achieving 94.7% (ground truth 96.3%) without data preprocessing or retraining on the hardware. We also determine the fundamental upper bound on throughput (~0.9 exaMAC/s), set by the maximum optical bandwidth before significant increase in error. Our combination of wide spectral and spatial bandwidths in a CMOS-compatible system enables highly efficient computing for next-generation DNNs.