# PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator

arXiv: https://arxiv.org/abs/2211.05276 · HTML: https://mlsanity.com/paper/2211.05276/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 78 references that are in the map.
- First posted on arXiv 10 Nov 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [CNN-Based Projected Gradient Descent for Consistent Image Reconstruction](https://mlsanity.com/paper/1709.01809/) (2017)
- [Bit-pragmatic Deep Neural Network Computing](https://mlsanity.com/paper/1610.06920/) (2016)
- [CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator](https://mlsanity.com/paper/2102.06960/) (2021)
- [SCALE-Sim: Systolic CNN Accelerator Simulator](https://mlsanity.com/paper/1811.02883/) (2018)
- [Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)](https://mlsanity.com/paper/1907.01525/) (2019)
- [Massively Parallel Amplitude-Only Fourier Neural Network](https://mlsanity.com/paper/2008.05853/) (2020)
- [PCNNA: A Photonic Convolutional Neural Network Accelerator](https://mlsanity.com/paper/1807.08792/) (2018)
- [SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis](https://mlsanity.com/paper/1803.00663/) (2018)

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

- [Training Neural Networks for Execution on Approximate Hardware](https://mlsanity.com/paper/2304.04125/) (2023)

## Abstract (arXiv)

The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potential to dramatically accelerate neural networks because of its low-latency nature. Combined with the concept of Joint Transform Correlator (JTC), the computationally expensive convolution functions can be computed instantaneously (time of flight of light) with almost no cost. This 'free' convolution computation provides the theoretical basis of the proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a myriad of challenges posed by on-chip photonic computing in the Fourier domain including 1D lenses and high-cost optoelectronic conversions. The proposed PhotoFourier accelerator achieves more than 28X better energy-delay product compared to state-of-art photonic neural network accelerators.