# GHOST: A Graph Neural Network Accelerator using Silicon Photonics

arXiv: https://arxiv.org/abs/2307.01782 · HTML: https://mlsanity.com/paper/2307.01782/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 52 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Computing Graph Neural Networks: A Survey from Algorithms to Accelerators](https://mlsanity.com/paper/2010.00130/) (2020)
- [HyGCN: A GCN Accelerator with Hybrid Architecture](https://mlsanity.com/paper/2001.02514/) (2020)
- [EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks](https://mlsanity.com/paper/1909.00155/) (2019)
- [GRIP: A Graph Neural Network Accelerator Architecture](https://mlsanity.com/paper/2007.13828/) (2020)
- [CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator](https://mlsanity.com/paper/2102.06960/) (2021)
- [A Survey on Silicon Photonics for Deep Learning](https://mlsanity.com/paper/2101.01751/) (2021)
- [Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)](https://mlsanity.com/paper/1907.01525/) (2019)
- [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)
- [ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks](https://mlsanity.com/paper/2102.07959/) (2021)
- [BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning](https://mlsanity.com/paper/2102.10140/) (2021)
- [Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing](https://mlsanity.com/paper/2303.12910/) (2023)
- [Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations](https://mlsanity.com/paper/2204.09153/) (2022)

## Built on by (most cited first, 7 of 7)

- [SafeLight: Enhancing Security in Optical Convolutional Neural Network Accelerators](https://mlsanity.com/paper/2411.16712/) (2024)
- [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)
- [PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics](https://mlsanity.com/paper/2501.13828/) (2025)
- [Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics](https://mlsanity.com/paper/2603.07626/) (2026)
- [Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Accelerators](https://mlsanity.com/paper/2609.06796/) (2026)

## Abstract (arXiv)

Graph neural networks (GNNs) have emerged as a powerful approach for modelling and learning from graph-structured data. Multiple fields have since benefitted enormously from the capabilities of GNNs, such as recommendation systems, social network analysis, drug discovery, and robotics. However, accelerating and efficiently processing GNNs require a unique approach that goes beyond conventional artificial neural network accelerators, due to the substantial computational and memory requirements of GNNs. The slowdown of scaling in CMOS platforms also motivates a search for alternative implementation substrates. In this paper, we present GHOST, the first silicon-photonic hardware accelerator for GNNs. GHOST efficiently alleviates the costs associated with both vertex-centric and edge-centric operations. It implements separately the three main stages involved in running GNNs in the optical domain, allowing it to be used for the inference of various widely used GNN models and architectures, such as graph convolution networks and graph attention networks. Our simulation studies indicate that GHOST exhibits at least 10.2x better throughput and 3.8x better energy efficiency when compared to GPU, TPU, CPU and multiple state-of-the-art GNN hardware accelerators.