KANELÉ: Kolmogorov–Arnold Networks for Efficient LUT-based Evaluation
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3748173.3779202.pdf
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Author(s)
Hoang, Duc
Gupta, Aarush
Harris, Philip C
Date Issued
February 21, 2026
Publisher
ACM|Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable Gate Arrays
Citation
Duc Hoang, Aarush Gupta, and Philip C Harris. 2026. KANELÉ: Kolmogorov–Arnold Networks for Efficient LUT-based Evaluation. In Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGA '26). Association for Computing Machinery, New York, NY, USA, 44–55.
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Final published version
Abstract
Low-latency, resource-efficient neural network inference on FPGAs is essential for applications demanding real-time capability and low power. Lookup table (LUT)-based neural networks are a common solution, combining strong representational power with efficient FPGA implementation. In this work, we introduce KANELÉ, a framework that exploits the unique properties of Kolmogorov–Arnold Networks (KANs) for FPGA deployment. Unlike traditional multilayer perceptrons (MLPs), KANs employ learnable one-dimensional splines with fixed domains as edge activations, a structure naturally suited to discretization and efficient LUT mapping. We present the first systematic design flow for implementing KANs on FPGAs, co-optimizing training with quantization and pruning to enable compact, high-throughput, and low-latency KAN architectures. Our results demonstrate up to a 2700x speedup and orders of magnitude resource savings compared to prior KAN-on-FPGA approaches. Moreover, KANELÉ matches or surpasses other LUT-based architectures on widely used benchmarks, particularly for tasks involving symbolic or physical formulas, while balancing resource usage across FPGA hardware. Finally, we showcase the versatility of the framework by extending it to real-time, power-efficient control systems.
Description
FPGA ’26, Seaside, CA, USA
MIT Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Physics
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DOI of Published Version
https://doi.org/10.1145/3748173.3779202
