Ultrafast On-chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks

2026-06-18 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

The work introduces an approach for ultrafast on-chip online learning using Kolmogorov-Arnold Networks (KANs) on Field-Programmable Gate Arrays (FPGAs). It demonstrates that KANs, unlike conventional Multi-Layer Perceptrons (MLPs), are highly efficient and numerically stable for high-frequency systems requiring sub-microsecond adaptation, such as controls for quantum computing and nuclear fusion. Key findings show that KAN updates exploit B-spline locality, leading to sparse updates and superior on-chip resource scaling. Furthermore, KANs are inherently robust to fixed-point quantization. Implemented on an AMD Virtex UltraScale+ XCVU13P FPGA at 200 MHz, KAN-based online learners achieve sub-100 ns latency for both forward and backward passes, outperforming MLPs across tasks like drifting regression, adaptive qubit readout, and non-stationary Acrobot control. This is the first demonstration of model-free online learning at sub-microsecond latencies.

Key takeaway

For AI Hardware Engineers designing systems for high-frequency, non-stationary environments, this research indicates that Kolmogorov-Arnold Networks (KANs) offer a superior alternative to MLPs. You should consider KANs for on-chip online learning applications requiring sub-microsecond adaptation, such as quantum control or plasma diagnostics, due to their sparse updates, fixed-point robustness, and efficient resource scaling on FPGAs. This enables stable, deterministic real-time adaptation where conventional dense MLPs fail.

Key insights

KANs enable ultrafast, stable on-chip online learning by leveraging B-spline locality and fixed-point quantization robustness.

Principles

• KAN updates exploiting B-spline locality are sparse, enabling superior on-chip resource scaling.
• KANs are inherently robust to fixed-point quantization, ensuring numerical stability.
• KAN capacity scales via grid size (G) at near-constant per-sample compute cost.

Method

The method involves implementing fixed-point online training of KANs on FPGAs, utilizing precomputed B-spline basis/derivative LUTs and index-driven updates of only the active coefficients.

In practice

• Apply KANs for real-time sensor calibration and signal recovery in non-stationary environments.
• Use KANs for adaptive single-shot qubit readout in quantum computing platforms.
• Implement KANs for online policy optimization in non-stationary control systems.

Topics

• Kolmogorov-Arnold Networks
• On-chip Learning
• FPGA Implementation
• Ultrafast Online Learning
• Fixed-Point Quantization
• Quantum Control
• B-spline Locality

Code references

• Duchstf/ECLAIR

Best for: Research Scientist, AI Scientist, AI Hardware Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.