Ultra Fast Transformers on FPGAs for Particle Physics Experiments

• URL: http://arxiv.org/abs/2402.01047v1
• Date: Thu, 1 Feb 2024 22:32:39 GMT
• Title: Ultra Fast Transformers on FPGAs for Particle Physics Experiments
• Authors: Zhixing Jiang, Dennis Yin, Elham E Khoda, Vladimir Loncar, Ekaterina Govorkova, Eric Moreno, Philip Harris, Scott Hauck, Shih-Chieh Hsu
• Abstract summary: This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) We have implemented critical components of a transformer model, such as multi-head attention and softmax layers. We recorded latency under 2 $mu$s on the Xilinx UltraScale+ FPGA, which is compatible with hardware trigger requirements at CERN.
• Score: 2.666074491398626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool. Given the demonstrated effectiveness of transformer models in addressing a wide range of problems, their application in experimental triggers within particle physics becomes a subject of significant interest. In this work, we have implemented critical components of a transformer model, such as multi-head attention and softmax layers. To evaluate the effectiveness of our implementation, we have focused on a particle physics jet flavor tagging problem, employing a public dataset. We recorded latency under 2 $\mu$s on the Xilinx UltraScale+ FPGA, which is compatible with hardware trigger requirements at the CERN Large Hadron Collider experiments.

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