mlsanityData to 7 Oct 2026

TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics

arXiv 2303.12914 ↗First posted 22 Mar 2023cs.LG cs.ARcited by 12

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Abstract (arXiv)

Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision. However, the complex structure of these models creates challenges for accelerating their execution on conventional electronic platforms. We propose the first silicon photonic hardware neural network accelerator called TRON for transformer-based models such as BERT, and Vision Transformers. Our analysis demonstrates that TRON exhibits at least 14x better throughput and 8x better energy efficiency, in comparison to state-of-the-art transformer accelerators.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 22 Mar 2023.

Builds on

11 of 30 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  5. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  6. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  7. 2020Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformercited by 42
  8. 2021Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimizationcited by 27
  9. 2022VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformercited by 27
  10. 2021A Survey on Silicon Photonics for Deep Learningcited by 23
  11. 2022RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonicscited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 1; 2026: 4.

12 papers.

  1. 2023GHOST: A Graph Neural Network Accelerator using Silicon Photonicscited by 7
  2. 2024SafeLight: Enhancing Security in Optical Convolutional Neural Network Acceleratorscited by 4
  3. 2024Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonicscited by 1
  4. 2024Silicon Photonic 2.5D Interposer Networks for Overcoming Communication Bottlenecks in Scale-out Machine Learning Hardware Acceleratorscited by 1
  5. 2024OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Accelerationcited by 1
  6. 2024ARTEMIS: A Mixed Analog-Stochastic In-DRAM Accelerator for Transformer Neural Networkscited by 1
  7. 2025PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonicscited by 1
  8. 2024Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directionscited by 0
  9. 2026Accelerating Diffusion Models for Generative AI Applications with Silicon Photonicscited by 0
  10. 2026DxPTA: An Architecture Design Space Exploration with Optical Dataflow-guided Strategy for HW/SW Co-Design of Photonic Transformer Acceleratorscited by 0
  11. 2026MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbarcited by 0
  12. 2026Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Acceleratorscited by 0