A study enabling ultra-low-latency inference and online learning on FPGAs using Kolmogorov-Arnold Networks (KAN) has been published. It achieves nanosecond-scale latency and high hardware efficiency compared to GPUs.
Research implementing ultra-fast inference and online learning on FPGAs using Kolmogorov-Arnold Networks (KAN) has been published. The work received the FPGA 2026 Best Paper award and was accepted at ICML 2026.
While GPUs excel at large-scale parallel processing, their instruction scheduling and memory access overhead make them unsuitable for sub-microsecond latency applications. FPGAs, as reconfigurable digital logic devices, can implement neural networks directly as digital circuits, achieving nanosecond-scale latency. KAN offers high expressivity with fewer parameters than MLPs and is particularly suited for LUT-based FPGA implementation.
This research opens the door for real-time AI inference and online learning on FPGAs, applicable to fields requiring extreme low latency such as high-energy physics, autonomous driving, and robotics. It demonstrates the hardware-friendliness of KAN architecture, suggesting a new direction for FPGA-based AI accelerators.
Comments noted that FPGA-based Kolmogorov-Arnold Networks are specialized for ultra-low-latency, ultra-low-power applications like high-energy physics, quantum computing, and plasma control with very small models, and are unsuitable for LLM inference acceleration. It was also mentioned that the author already works at Jane Street, raising connections to high-frequency trading (HFT), with some suggesting the technology could generate enormous profits if applied to HFT. There was also discussion about regional access differences in the original post.