Summary of Ultra Fast Transformers on Fpgas For Particle Physics Experiments, by Zhixing Jiang et al.
Ultra Fast Transformers on FPGAs for Particle Physics Experiments
by Zhixing Jiang, Dennis Yin, Elham E Khoda, Vladimir Loncar, Ekaterina Govorkova, Eric Moreno, Philip Harris, Scott Hauck, Shih-Chieh Hsu
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); High Energy Physics – Experiment (hep-ex)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) using the hls4ml tool. This implementation enables the application of transformer models in experimental triggers within particle physics, addressing a wide range of problems. The key components of a transformer model, including multi-head attention and softmax layers, are implemented and evaluated using a public dataset for a particle physics jet flavor tagging problem. The achieved latency of under 2 μs on the Xilinx UltraScale+ FPGA meets hardware trigger requirements at the CERN Large Hadron Collider experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a special kind of computer chip, called an FPGA, work better with something called transformer models. These models are good at solving certain problems and scientists want to use them in their research. The people who wrote this paper made some important parts of the model work on the chip and tested it using real data from experiments. They were able to make it work fast enough for big machines that collect lots of data. |
Keywords
* Artificial intelligence * Multi head attention * Softmax * Transformer
