用MLP-Mixer实现低延迟喷注识别，适配硬件受限的实时场景。
Fast Jet Tagging with MLP-Mixers on FPGAs
- 采用MLP-Mixer处理喷注组分序列，支持高效特征提取。
- 资源占用降低97%，吞吐量翻倍，延迟减半，精度达顶尖水平。
- 非置换不变结构利于特征优先级调度，适合对时延敏感的对撞机应用。
我们探索了将MLP-Mixer模型应用于实时喷注识别的创新方法，并验证其在资源受限硬件如FPGA上的可行性。MLP-Mixer在模拟大型强子对撞机条件的数据集上表现优异，达到当前最优性能。通过高粒度量化和分布式算术等优化技术，实现了前所未有的效率：模型精度与此前架构相当或更优，硬件资源使用减少高达97%，吞吐量提升一倍，延迟降低一半。此外，非置换不变架构支持智能特征优先处理，推动机器学习在粒子对撞机实时数据处理中的新标杆。
原文摘要 · Abstract (English)
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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