Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml

• URL: http://arxiv.org/abs/2409.05207v1
• Date: Sun, 8 Sep 2024 19:50:25 GMT
• Title: Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml
• Authors: Zhixing Jiang, Dennis Yin, Yihui Chen, Elham E Khoda, Scott Hauck, Shih-Chieh Hsu, Ekaterina Govorkova, Philip Harris, Vladimir Loncar, Eric A. Moreno,
• Abstract summary: This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml. Their deployment on VU13P FPGA chip achieved less than 2us, demonstrating the potential for real-time applications.
• Score: 2.6892725687961394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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