# A Survey on Silicon Photonics for Deep Learning

arXiv: https://arxiv.org/abs/2101.01751 · HTML: https://mlsanity.com/paper/2101.01751/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2021.

## Built on by (most cited first, 23 of 23)

- [ROBIN: A Robust Optical Binary Neural Network Accelerator](https://mlsanity.com/paper/2107.05530/) (2021)
- [RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics](https://mlsanity.com/paper/2209.00084/) (2022)
- [TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics](https://mlsanity.com/paper/2303.12914/) (2023)
- [SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning](https://mlsanity.com/paper/2109.04459/) (2021)
- [A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization](https://mlsanity.com/paper/2205.11244/) (2022)
- [GHOST: A Graph Neural Network Accelerator using Silicon Photonics](https://mlsanity.com/paper/2307.01782/) (2023)
- [Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics](https://mlsanity.com/paper/2301.12252/) (2023)
- [Characterizing Coherent Integrated Photonic Neural Networks under Imperfections](https://mlsanity.com/paper/2207.10835/) (2022)
- [Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis](https://mlsanity.com/paper/2112.07485/) (2021)
- [FatNet: High Resolution Kernels for Classification Using Fully Convolutional Optical Neural Networks](https://mlsanity.com/paper/2210.16914/) (2022)
- [Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing](https://mlsanity.com/paper/2303.12910/) (2023)
- [Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics](https://mlsanity.com/paper/2401.06885/) (2024)
- [Silicon Photonic 2.5D Interposer Networks for Overcoming Communication Bottlenecks in Scale-out Machine Learning Hardware Accelerators](https://mlsanity.com/paper/2403.04189/) (2024)
- [OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration](https://mlsanity.com/paper/2407.08205/) (2024)
- [Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks](https://mlsanity.com/paper/2308.03249/) (2023)
- [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)
- [Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics](https://mlsanity.com/paper/2603.07626/) (2026)
- [Extraction of linearized models from pre-trained networks via knowledge distillation](https://mlsanity.com/paper/2604.06732/) (2026)
- [DxPTA: An Architecture Design Space Exploration with Optical Dataflow-guided Strategy for HW/SW Co-Design of Photonic Transformer Accelerators](https://mlsanity.com/paper/2606.06515/) (2026)
- [MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar](https://mlsanity.com/paper/2607.26016/) (2026)

## Abstract (arXiv)

Deep learning has led to unprecedented successes in solving some very difficult problems in domains such as computer vision, natural language processing, and general pattern recognition. These achievements are the culmination of decades-long research into better training techniques and deeper neural network models, as well as improvements in hardware platforms that are used to train and execute the deep neural network models. Many application-specific integrated circuit (ASIC) hardware accelerators for deep learning have garnered interest in recent years due to their improved performance and energy-efficiency over conventional CPU and GPU architectures. However, these accelerators are constrained by fundamental bottlenecks due to 1) the slowdown in CMOS scaling, which has limited computational and performance-per-watt capabilities of emerging electronic processors, and 2) the use of metallic interconnects for data movement, which do not scale well and are a major cause of bandwidth, latency, and energy inefficiencies in almost every contemporary processor. Silicon photonics has emerged as a promising CMOS-compatible alternative to realize a new generation of deep learning accelerators that can use light for both communication and computation. This article surveys the landscape of silicon photonics to accelerate deep learning, with a coverage of developments across design abstractions in a bottom-up manner, to convey both the capabilities and limitations of the silicon photonics paradigm in the context of deep learning acceleration.