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.

翻译：我们探索了MLP-Mixer模型在实时喷注标记中的创新应用，并验证了其在FPGA等资源受限硬件上的可行性。MLP-Mixer在处理喷注成分序列方面表现卓越，在模拟大型强子对撞机条件的数据集上达到了最先进的性能。通过采用高粒度量化与分布式算术等先进优化技术，我们实现了前所未有的效率。这些模型在精度上达到或超越了先前架构，同时将硬件资源使用量降低高达97%，吞吐量提升一倍，延迟减半。此外，非置换不变架构实现了智能特征优先级排序与高效的FPGA部署，为粒子对撞机实时数据处理的机器学习应用设立了新标杆。