AI RESEARCH
Ultrafast On-chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks
arXiv CS.LG
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ArXi:2602.02056v2 Announce Type: replace-cross 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 Kolmogoro-Arnold Networks (KANs) that align with these constraints.