We study various machine learning based algorithms for performing accurate
jet flavor classification on field-programmable gate arrays and demonstrate how
latency and resource consumption scale with the input size and choice of
algorithm. These architectures provide an initial design for models that could
be used for tagging at the CERN LHC during its high-luminosity phase. The
high-luminosity upgrade will lead to a five-fold increase in its instantaneous
luminosity for proton-proton collisions and, in turn, higher data volume and
complexity, such as the availability of jet constituents. Through
quantization-aware training and efficient hardware implementations, we show
that O(100) ns inference of complex architectures such as deep sets and
interaction networks is feasible at a low computational resource cost.

通过对基于机器学习的算法在可编程门阵列上进行准确的喷注风味分类的研究，展示了在输入规模和算法选择方面的延迟和资源消耗如何扩展，并提供了一种用于在 CERN LHC 的高亮度阶段进行标记的模型的初始设计。通过量化感知训练和高效硬件实现，我们展示了可以以较低的计算资源成本实现复杂架构（例如深度集合和交互网络）的 O（100）纳秒推理。