# Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)

arXiv: https://arxiv.org/abs/1907.01525 · HTML: https://mlsanity.com/paper/1907.01525/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 22 of 22)

- [CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator](https://mlsanity.com/paper/2102.06960/) (2021)
- [Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment](https://mlsanity.com/paper/2111.06862/) (2021)
- [ROBIN: A Robust Optical Binary Neural Network Accelerator](https://mlsanity.com/paper/2107.05530/) (2021)
- [Dynamic Precision Analog Computing for Neural Networks](https://mlsanity.com/paper/2102.06365/) (2021)
- [GHOST: A Graph Neural Network Accelerator using Silicon Photonics](https://mlsanity.com/paper/2307.01782/) (2023)
- [Photonic Reconfigurable Accelerators for Efficient Inference of CNNs with Mixed-Sized Tensors](https://mlsanity.com/paper/2207.05278/) (2022)
- [Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics](https://mlsanity.com/paper/2301.12252/) (2023)
- [SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs](https://mlsanity.com/paper/2302.07036/) (2023)
- [An Optical XNOR-Bitcount Based Accelerator for Efficient Inference of Binary Neural Networks](https://mlsanity.com/paper/2302.06405/) (2023)
- [Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing](https://mlsanity.com/paper/2303.12910/) (2023)
- [RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement](https://mlsanity.com/paper/2207.06883/) (2022)
- [PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator](https://mlsanity.com/paper/2211.05276/) (2022)
- [High-Speed and Energy-Efficient Non-Binary Computing with Polymorphic Electro-Optic Circuits and Architectures](https://mlsanity.com/paper/2304.07608/) (2023)
- [Photonics for Sustainable Computing](https://mlsanity.com/paper/2401.05121/) (2024)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets](https://mlsanity.com/paper/2210.08004/) (2022)
- [Towards Efficient Hyperdimensional Computing Using Photonics](https://mlsanity.com/paper/2311.17801/) (2023)
- [A Comparative Analysis of Microrings Based Incoherent Photonic GEMM Accelerators](https://mlsanity.com/paper/2402.03149/) (2024)
- [HEANA: A Hybrid Time-Amplitude Analog Optical Accelerator with Flexible Dataflows for Energy-Efficient CNN Inference](https://mlsanity.com/paper/2402.03247/) (2024)
- [Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions](https://mlsanity.com/paper/2407.21184/) (2024)
- [All-optical temporal integration mediated by subwavelength heat antennas](https://mlsanity.com/paper/2505.04405/) (2025)
- [ROSA: Robust and Energy-Efficient Microring-Based Optical Neural Networks via Optical Shift-and-Add and Layer-Wise Hybrid Mapping](https://mlsanity.com/paper/2605.00032/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing. However, a convolution is a computationally expensive operation in digital electronics. In contrast, neuromorphic photonic systems, which have experienced a recent surge of interest over the last few years, propose higher bandwidth and energy efficiencies for neural network training and inference. Neuromorphic photonics exploits the advantages of optical electronics, including the ease of analog processing, and busing multiple signals on a single waveguide at the speed of light. Here, we propose a Digital Electronic and Analog Photonic (DEAP) CNN hardware architecture that has potential to be 2.8 to 14 times faster while maintaining the same power usage of current state-of-the-art GPUs.