Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml

Zhixing Jiang; Dennis Yin; Yihui Chen; Elham E Khoda; Scott Hauck; Shih-Chieh Hsu; Ekaterina Govorkova; Philip Harris; Vladimir Loncar; Eric A. Moreno

arXiv · 2409.05207

Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml

Zhixing Jiang·Dennis Yin·Yihui Chen·Elham E Khoda·Scott Hauck·Shih-Chieh Hsu·Ekaterina Govorkova·Philip Harris·Vladimir Loncar·Eric A. Moreno

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Abstract

This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml. We demonstrate the strategy for implementing the multi-head attention, softmax, and normalization layer and evaluate three distinct models. Their deployment on VU13P FPGA chip achieved latency less than 2us, demonstrating the potential for real-time applications. HLS4ML compatibility with any TensorFlow-built transformer model further enhances the scalability and applicability of this work. Index Terms: FPGAs, machine learning, transformers, high energy physics, LIGO

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Zhixing Jiang, Dennis Yin, Yihui Chen, Elham E Khoda, Scott Hauck, Shih-Chieh Hsu, Ekaterina Govorkova, Philip Harris, Vladimir Loncar, Eric A. Moreno. 2024-09-08. Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml. https://arxiv.org/abs/2409.05207

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