# High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator

arXiv: https://arxiv.org/abs/2112.12297 · HTML: https://mlsanity.com/paper/2112.12297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 23 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)
- [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)
- [Massively Parallel Amplitude-Only Fourier Neural Network](https://mlsanity.com/paper/2008.05853/) (2020)
- [Ensemble learning of diffractive optical networks](https://mlsanity.com/paper/2009.06869/) (2020)
- [Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy](https://mlsanity.com/paper/1906.03417/) (2019)
- [Channel Tiling for Improved Performance and Accuracy of Optical Neural Network Accelerators](https://mlsanity.com/paper/2011.07391/) (2020)
- [Electronic Bottleneck Suppression in Next-generation Networks with Integrated Photonic Digital-to-analog Converters](https://mlsanity.com/paper/1911.02511/) (2019)
- [Programmable Silicon Photonic Optical Thresholder](https://mlsanity.com/paper/1908.07067/) (2019)

## Built on by (most cited first, 1 of 1)

- [Neural Tangent Knowledge Distillation for Optical Convolutional Networks](https://mlsanity.com/paper/2508.08421/) (2025)

## Abstract (arXiv)

Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today. With convolution operations being computationally intensive, next generation hardware accelerators need to offer parallelization and algorithmic-hardware homomorphism. Fortunately, diffractive display optics is capable of million-channel parallel data processing at low latency, however, thus far only showed tens of Hertz slow single image and kernel capability, thereby significantly underdelivering from its performance potential. Here, we demonstrate an operation-parallelized high-throughput Fourier optic convolutional neural network accelerator. For the first time simultaneously processing of multiple kernels in Fourier domain enabled by optical diffraction has been achieved alongside with already conventional in the field input parallelism. Additionally, we show an about one hundred times system speed up over existing optical diffraction-based processors and this demonstration rivals performance of modern electronic solutions. Therefore, this system is capable of processing large-scale matrices about ten times faster than state of art electronic systems.