Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales. Meeting these requirements demands low-latency, fixed-precision computation under strict memory constraints, a regime in which conventional Multi-Layer Perceptrons (MLPs) are both inefficient and numerically unstable. We identify key properties of Kolmogorov-Arnold Networks (KANs) that align with these constraints. Specifically, we show that: (i) KAN updates exploiting B-spline locality are sparse, enabling superior on-chip resource scaling, and (ii) KANs are inherently robust to fixed-point quantization. By implementing fixed-point online training on Field-Programmable Gate Arrays (FPGAs), a representative platform for on-chip computation, we demonstrate that KAN-based online learners are significantly more efficient and expressive than MLPs across a range of low-latency and resource-constrained tasks. To our knowledge, this work is the first to demonstrate model-free online learning at sub-microsecond latencies.

翻译：超快在线学习对高频系统至关重要，例如量子计算和核聚变中的控制系统，这些系统的自适应调整必须在亚微秒时间尺度内完成。满足这些要求需要在严格的内存约束下实现低延迟、固定精度的计算，而传统的多层感知器（MLPs）在这种条件下既效率低下又数值不稳定。我们识别出Kolmogorov-Arnold网络（KANs）中与这些约束相一致的关键特性。具体而言，我们证明：（i）利用B样条局部性的KAN更新是稀疏的，从而在片上实现了优越的资源扩展能力；（ii）KANs天然对定点量化具有鲁棒性。通过在代表片上计算平台的现场可编程门阵列（FPGAs）上实现定点在线训练，我们证明基于KAN的在线学习器在一系列低延迟和资源受限的任务中比MLPs显著更高效且更具表达力。据我们所知，本工作是首次在亚微秒延迟下实现无模型在线学习的演示。