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Welcome to Lightning, the next-generation photonic computing system.

Lightning is a reconfigurable photonic-electronic smartNIC that serves real-time deep neural network inference requests at 100 Gbps. Lightning uses a novel datapath that feeds traffic from the NIC into the photonic domain without creating digital packet processing and data movement bottlenecks. Lightning achieves this by employing a reconfigurable count-action abstraction, which keeps track of the computation Directed Acyclic Graph (DAG) of each inference packet. Our count-action abstraction decouples the compute control plane from the data plane by counting the number of operations in each task of the DAG and triggers the execution of the next tasks as soon as the previous task is finished, without interrupting the flow of the data.

 

To know more about this project:

 

Technical publications

[1] Lightning: A Reconfigurable Photonic-Electronic SmartNIC for Fast and Energy-Efficient Inference

Zhizhen Zhong, Mingran Yang, Jay Lang, Christian Williams, Liam Kronman, Alexander Sludds, Homa Esfahanizadeh, Dirk Englund, Manya Ghobadi

ACM SIGCOMM 2023

Paper | Artifact | Demo | Slides | Video 

MIT News article: System combines light and electrons to unlock faster, greener computing

 

[2] On-Fiber Photonic Computing

Mingran Yang*, Zhizhen Zhong*, Manya Ghobadi (co-first author)

ACM HotNets 2023 

Paper  

 

[3] Delocalized Photonic Deep Learning on the Internet's Edge

Alexander Sludds, Saumil Bandyopadhyay, Zaijun Chen, Zhizhen Zhong, Jared Cochrane, Liane Bernstein, Darius Bunandar, P. Ben Dixon, Scott Hamilton, Matthew Streshinsky, Ari Novack, Tom Baehr-Jones, Michael Hochberg, Manya Ghobadi, Ryan Hamerly, Dirk Englund

Science, Vol. 378 (6617) 2022

Paper | Artifact 

Best Paper Award at OECC 2022

 

[4] NetCast: Low-Power Edge Computing with WDM-defined Optical Neural Networks

Ryan Hamerly, Alexander Sludds, Saumil Bandyopadhyay, Zaijun Chen, Zhizhen Zhong, Liane Bernstein, Dirk Englund

arXiv:2207.01777

Paper 

 

[5] IOI: In-Network Optical Inference

Zhizhen Zhong, Weiyang Wang, Manya Ghobadi, Alexander Sludds, Ryan Hamerly, Liane Bernstein, Dirk Englund

ACM SIGCOMM 2021 Workshop on Optical Systems (OptSys 2021)

Paper | Slides