Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
23 of 32 references.
References · 23
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2023A Survey on Evaluation of Large Language Modelscited by 1,242
- 2020HyGCN: A GCN Accelerator with Hybrid Architecturecited by 74
- 2019EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networkscited by 43
- 2020Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformercited by 43
- 2020GRIP: A Graph Neural Network Accelerator Architecturecited by 37
- 2021CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Acceleratorcited by 29
- 2021Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimizationcited by 27
- 2022VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformercited by 27
- 2021A Survey on Silicon Photonics for Deep Learningcited by 23
11 more
- 2021ROBIN: A Robust Optical Binary Neural Network Acceleratorcited by 15
- 2022RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonicscited by 12
- 2023TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonicscited by 12
- 2021SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learningcited by 10
- 2022A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantizationcited by 9
- 2023GHOST: A Graph Neural Network Accelerator using Silicon Photonicscited by 7
- 2021ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networkscited by 5
- 2023Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonicscited by 5
- 2022LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networkscited by 4
- 2023Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computingcited by 3
- 2021CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networkscited by 2
Built on by
Citing papers per year: 2024: 1.
1 paper.