# Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator

arXiv: https://arxiv.org/abs/2305.19533 · HTML: https://mlsanity.com/paper/2305.19533/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2023.

## Built on by (most cited first, 15 of 15)

- [A Survey on Inference Optimization Techniques for Mixture of Experts Models](https://mlsanity.com/paper/2412.14219/) (2024)
- [TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator](https://mlsanity.com/paper/2402.07393/) (2024)
- [PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices](https://mlsanity.com/paper/2411.03527/) (2024)
- [Neural Network Methods for Radiation Detectors and Imaging](https://mlsanity.com/paper/2311.05726/) (2023)
- [Towards Efficient Hyperdimensional Computing Using Photonics](https://mlsanity.com/paper/2311.17801/) (2023)
- [Toward Large-Scale Photonics-Empowered AI Systems: From Physical Design Automation to System-Algorithm Co-Exploration](https://mlsanity.com/paper/2601.00129/) (2025)
- [Democratizing Electronic-Photonic AI Systems: An Open-Source AI-Infused Cross-Layer Co-Design and Design Automation Toolflow](https://mlsanity.com/paper/2601.00130/) (2025)
- [Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics](https://mlsanity.com/paper/2603.07626/) (2026)
- [PRISM: Breaking the O(n) Memory Wall in Long-Context LLM Inference via O(1) Photonic Block Selection](https://mlsanity.com/paper/2603.21576/) (2026)
- [Harnessing Photonics for Machine Intelligence](https://mlsanity.com/paper/2604.10841/) (2026)
- [ROSA: Robust and Energy-Efficient Microring-Based Optical Neural Networks via Optical Shift-and-Add and Layer-Wise Hybrid Mapping](https://mlsanity.com/paper/2605.00032/) (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)
- [Attention by Synchronization in Coupled Oscillator Networks](https://mlsanity.com/paper/2606.12059/) (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)
- [AIR-LLM: Broadcasting AI Weights over Radio for Memory-Free Edge LLM Inference via RF Computing](https://mlsanity.com/paper/2610.00465/) (2026)

## Abstract (arXiv)

The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficient hardware accelerators. There is a growing interest in exploring photonics as an alternative technology to digital electronics due to its high energy efficiency and ultra-fast processing speed. Photonic accelerators have shown promising results for CNNs, which mainly rely on weight-static linear operations. However, they encounter issues when efficiently supporting Transformer architectures, questioning the applicability of photonics to advanced ML tasks. The primary hurdle lies in their inefficiency in handling unique workloads in Transformers, i.e., dynamic and full-range tensor multiplication. In this work, we propose Lightening-Transformer, the first light-empowered, high-performance, and energy-efficient photonic Transformer accelerator. To overcome prior designs' fundamental limitations, we introduce a novel dynamically-operated photonic tensor core, DPTC, a crossbar array of interference-based optical vector dot-product engines supporting highly parallel, dynamic, and full-range matrix multiplication. Furthermore, we design a dedicated accelerator that integrates our novel photonic computing cores with photonic interconnects for inter-core data broadcast, fully unleashing the power of optics. Comprehensive evaluations show that ours achieves >2.6x energy and >12x latency reductions compared to prior photonic accelerators and delivers the lowest energy cost and 2 to 3 orders of magnitude lower energy-delay product compared to electronic Transformer accelerators, all while maintaining digital-comparable accuracy. Our work highlights the immense potential of photonics for advanced ML workloads, such as Transformer-backboned LLM. Our work is available at https://github.com/zhuhanqing/Lightening-Transformer.