Fast Jet Tagging with MLP-Mixers on FPGAs

• URL: http://arxiv.org/abs/2503.03103v1
• Date: Wed, 05 Mar 2025 01:37:47 GMT
• Title: Fast Jet Tagging with MLP-Mixers on FPGAs
• Authors: Chang Sun, Jennifer Ngadiuba, Maurizio Pierini, Maria Spiropulu,
• Abstract summary: We explore the innovative use of Distributed-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs.<n>We achieve state-of-the-art performance on datasets mimicking Large Hadron Collider conditions.<n>Non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.
• Score: 1.5087357654868088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.

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