KAN网络实现微秒级在线学习，比传统模型更高效稳定。
Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks
- 利用样条局部性实现稀疏更新，节省芯片资源
- 固定精度下仍保持数值稳定，支持低延迟计算
- 首次在微秒级实现无模型在线学习，适合量子控制等场景
超快在线学习对高频系统（如量子计算与核聚变控制）至关重要，要求在亚微秒级时间内完成自适应。这需要在严格内存约束下实现低延迟、固定精度计算，而传统多层感知机（MLPs）在此场景下既效率低下又数值不稳定。我们发现柯尔莫哥洛夫-阿诺德网络（KANs）的关键特性与此需求高度匹配：(i) 利用B样条局部性进行的KAN更新具有稀疏性，支持优异的芯片资源扩展；(ii) KAN天然对定点量化具有鲁棒性。通过在可编程门阵列（FPGAs）上实现定点在线训练，我们证明，在多种低延迟、资源受限任务中，基于KAN的在线学习器比MLPs更具效率和表达能力。据我们所知，这是首个实现亚微秒级无模型在线学习的工作。
原文摘要 · Abstract (English)
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.
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