# Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics

arXiv: https://arxiv.org/abs/2401.06885 · HTML: https://mlsanity.com/paper/2401.06885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 12 Jan 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [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)
- [Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer](https://mlsanity.com/paper/2009.08605/) (2020)
- [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)
- [Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization](https://mlsanity.com/paper/2110.10030/) (2021)
- [VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer](https://mlsanity.com/paper/2201.06618/) (2022)
- [A Survey on Silicon Photonics for Deep Learning](https://mlsanity.com/paper/2101.01751/) (2021)
- [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)
- [ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks](https://mlsanity.com/paper/2102.07959/) (2021)
- [Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics](https://mlsanity.com/paper/2301.12252/) (2023)
- [LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks](https://mlsanity.com/paper/2204.03835/) (2022)
- [Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing](https://mlsanity.com/paper/2303.12910/) (2023)
- [CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networks](https://mlsanity.com/paper/2112.06098/) (2021)

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

- [SafeLight: Enhancing Security in Optical Convolutional Neural Network Accelerators](https://mlsanity.com/paper/2411.16712/) (2024)

## Abstract (arXiv)

In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) and graph processing have emerged as transformative technologies for natural language processing (NLP), computer vision, and graph-structured data applications. However, the complex structures of these models pose challenges for acceleration on conventional electronic platforms. In this paper, we describe novel hardware accelerators based on silicon photonics to accelerate transformer neural networks that are used in LLMs and graph neural networks for graph data processing. Our analysis demonstrates that both hardware accelerators achieve at least 10.2x throughput improvement and 3.8x better energy efficiency over multiple state-of-the-art electronic hardware accelerators designed for LLMs and graph processing.