Welcome to the Lightning, the first reconfigurable photonic-electronic neural network inference system integrated with the a 100 Gbps smartNIC.
We propose Lightning, the first reconfigurable photonic-electronic smartNIC to serve real-time deep neural network inference requests. Lightning uses a fast datapath to feed traffic from the NIC into the photonic domain without creating digital packet processing and data movement bottlenecks. To do so, Lightning leverages a novel reconfigurable count-action abstraction that keeps track of the required computation operations 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 and triggers the execution of the next task(s) withou 8000 t interrupting the dataflow. To the best of our knowledge, our prototype is the highest-frequency photonic computing system, capable of serving real-time inference queries at 4.055 GHz end-to-end.
For a full technical description on Lightning, please read our ACM SIGCOMM 2023 paper and demo:
Z. Zhong, M. Yang, J. Lang, C. Williams, L. Kronman, A. Sludds, H. Esfahanizadeh, D. Englund, M. Ghobadi, "Lightning: A Reconfigurable Photonic-Electronic SmartNIC for Fast and Energy-Efficient Inference," ACM SIGCOMM, 2023. https://doi.org/10.1145/3603269.3604821
Z. Zhong, M. Yang, J. Lang, D. Englund, M. Ghobadi, "Demo: First Demonstration of Real-Time Photonic-Electronic DNN Acceleration on SmartNICs," ACM SIGCOMM, 2023. https://doi.org/10.1145/3603269.3610842
For more details on Lightning, please visit our website: https://lightning.mit.edu
This is an active project, if you want to have a community discussion, please start a new discussion thread in the discussion tab, and we will get back to you as soon as possible.
For any questions, please contact Zhizhen Zhong at zhizhenz [at] mit.edu. We welcome all contributions and feedbacks.
This repo has submodules, please clone the repo using git recursive clone.
git clone --recursive
This part of artifact contains Lightning's RTL-based datapath design and implementation (Sections 4, 5, and 6 of the Lightning SIGCOMM paper). We also include an emulated photonic MAC core to build a cycle-accurate testbench using Verilator.
| Source Files | Description |
|---|---|
rtl/datapath/ |
This folder contains the code of Lightning's datapath modules (packet I/O, memory controller, count-action logic, etc.) |
rtl/emulate/ |
This folder contains the code of emulated photonic multiplier modules |
rtl/sram/ |
This folder contains the code of SRAM modules |
rtl/tb/ |
This folder contains the code of Verilator-based testbench modules |
rtl/utils/ |
This folder contains the code of customized AXI-related modules and third-party AXI libraries |
rtl/Makefile |
This folder contains the Makefile for running the Verilator-based testbench |
rtl/README.md |
This README file explains the dependencies and steps to run the RTL cycle-accurate testbench |
This part of artifact contains the FPGA firmware and library code to enable Lightning's Python API (Section 6 and Appendix G of the Lightning SIGCOMM paper).
| Source Files | Description |
|---|---|
api/firmware/ |
This folder contains the code of the FPGA firmwares to support the Python API |
api/lightning_lib/ |
This folder contains the code of Lightning Python API libraries |
This part of artifact contains Lightning's photonic emulation (Section 7 of the Lightning SIGCOMM paper).
| Source Files | Description |
|---|---|
emulation/ |
This folder contains the code for running photonic emulation on large deep neural networks |
emulation/README.md |
This file contains the dependencies and steps to run the emulation code |
This part of artifact contains Lightning's event-driven simulation study on seven real-world large DNN models (Section 9 of the Lightning SIGCOMM paper).
| Source Files | Description |
|---|---|
simulation/ |
This folder contains the code for running event-driven simulations on DNN inference queries |
simulation/README.md |
This file contains the dependencies and steps to run the simulations code |
This part of artifact contains manufacture files for the Lightning developer kit (Appendix G of the Lightning SIGCOMM paper).
| Source Files | Description |
|---|---|
kit/laser_cutting/ |
This folder contains the design files for manufactoring the package of the developer kit using laser-cutting machines |
kit/3D_printing/ |
This folder contains the design files for manufactoring the device support components using 3D printers |
kit/lightning_devkit_v2.jpg |
This file contains the shopping list to assembly the open-source developer kit |
This part of artifact contains the data to reproduce our results.
| Source Files | Description |
|---|---|
data/saved_dataset/ |
This folder stores the datasets for corresponding DNN models |
data/saved_models/ |
This folder stores the considered DNN models |
This part of artifact contains automatic github actions like the DNN accuracy checker through our Verilator-based testbench.
| Source Files | Description |
|---|---|
.github/workflows/ |
This folder stores the automatic workflows like DNN accuracy checker |
- Xilinx Vivado 2022.2
- Xilinx PetaLinux 2022.2
- Ubuntu 20.04.6 LTS (Focal Fossa)
- Verilator 4.038 2020-07-11 rev v4.036-114-g0cd4a57ad
- Python 3.10.8
- Detailed dependencies is described in the README.md of each folder
Lightning is MIT-licensed.