# KANEL'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

arXiv: https://arxiv.org/abs/2512.12850 · HTML: https://mlsanity.com/paper/2512.12850/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 14 Dec 2025.

## Builds on (most cited first, 20 of 20)

- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [KAN 2.0: Kolmogorov-Arnold Networks Meet Science](https://mlsanity.com/paper/2408.10205/) (2024)
- [Convolutional Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2406.13155/) (2024)
- [KAN or MLP: A Fairer Comparison](https://mlsanity.com/paper/2407.16674/) (2024)
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.07344/) (2024)
- [A Survey on Kolmogorov-Arnold Network](https://mlsanity.com/paper/2411.06078/) (2024)
- [Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks](https://mlsanity.com/paper/2406.11045/) (2024)
- [LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications](https://mlsanity.com/paper/2004.03021/) (2020)
- [Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML](https://mlsanity.com/paper/2003.06308/) (2020)
- [On the expressiveness and spectral bias of KANs](https://mlsanity.com/paper/2410.01803/) (2024)
- [PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference](https://mlsanity.com/paper/2309.02334/) (2023)
- [Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability](https://mlsanity.com/paper/2407.11075/) (2024)
- [NeuraLUT: Hiding Neural Network Density in Boolean Synthesizable Functions](https://mlsanity.com/paper/2403.00849/) (2024)
- [TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees](https://mlsanity.com/paper/2501.01511/) (2025)
- [Weightless Neural Networks for Efficient Edge Inference](https://mlsanity.com/paper/2203.01479/) (2022)
- [PolyLUT-Add: FPGA-based LUT Inference with Wide Inputs](https://mlsanity.com/paper/2406.04910/) (2024)
- [da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs](https://mlsanity.com/paper/2507.04535/) (2025)
- [FourierKAN outperforms MLP on Text Classification Head Fine-tuning](https://mlsanity.com/paper/2408.08803/) (2024)

## Built on by (most cited first, 13 of 13)

- [KANtize: Exploring Low-bit Quantization of Kolmogorov-Arnold Networks for Efficient Inference](https://mlsanity.com/paper/2603.17230/) (2026)
- [Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation](https://mlsanity.com/paper/2512.18921/) (2025)
- [WARP Logic Neural Networks](https://mlsanity.com/paper/2602.03527/) (2026)
- [HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference](https://mlsanity.com/paper/2604.22293/) (2026)
- [FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs](https://mlsanity.com/paper/2607.08427/) (2026)
- [Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2602.02056/) (2026)
- [Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units](https://mlsanity.com/paper/2602.07518/) (2026)
- [From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips](https://mlsanity.com/paper/2603.22770/) (2026)
- [SparseKAN: Compressing Kolmogorov--Arnold Networks Across Basis Functions, Neurons, and Bits](https://mlsanity.com/paper/2608.00859/) (2026)
- [BiKAN: Restoring Collapsed Basis of Binary Kolmogorov--Arnold Networks](https://mlsanity.com/paper/2608.01490/) (2026)
- [Bern2Edge: A Neurosymbolic Compiler for Edge Deployment via Bernstein Polynomial Networks](https://mlsanity.com/paper/2608.20497/) (2026)
- [DiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity](https://mlsanity.com/paper/2609.09254/) (2026)
- [FuncCode: Compressing Kolmogorov--Arnold Networks in Function Space with Hardware-Aware Quantization](https://mlsanity.com/paper/2609.26067/) (2026)

## Abstract (arXiv)

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 KANEL'E, 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, KANEL'E 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.