Physical Analog Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units

• URL: http://arxiv.org/abs/2602.07518v1
• Date: Sat, 07 Feb 2026 12:33:11 GMT
• Title: Physical Analog Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units
• Authors: Manuel Escudero, Mohamadreza Zolfagharinejad, Sjoerd van den Belt, Nikolaos Alachiotis, Wilfred G. van der Wiel,
• Abstract summary: Kolmogorov-Arnold Networks (KANs) shift neural computation from linear layers to learnable nonlinear edge functions.<n>Here we introduce a physical analog KAN architecture in which edge functions are realized in materia using reconfigurable nonlinear-processing units (RNPUs)<n>We establish a realistic system-level hardware implementation that enables compact KAN-style regression and classification with programmable nonlinear transformations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Kolmogorov-Arnold Networks (KANs) shift neural computation from linear layers to learnable nonlinear edge functions, but implementing these nonlinearities efficiently in hardware remains an open challenge. Here we introduce a physical analog KAN architecture in which edge functions are realized in materia using reconfigurable nonlinear-processing units (RNPUs): multi-terminal nanoscale silicon devices whose input-output characteristics are tuned via control voltages. By combining multiple RNPUs into an edge processor and assembling these blocks into a reconfigurable analog KAN (aKAN) architecture with integrated mixed-signal interfacing, we establish a realistic system-level hardware implementation that enables compact KAN-style regression and classification with programmable nonlinear transformations. Using experimentally calibrated RNPU models and hardware measurements, we demonstrate accurate function approximation across increasing task complexity while requiring fewer or comparable trainable parameters than multilayer perceptrons (MLPs). System-level estimates indicate an energy per inference of $\sim$250 pJ and an end-to-end inference latency of $\sim$600 ns for a representative workload, corresponding to a $\sim$10$^{2}$-10$^{3}\times$ reduction in energy accompanied by a $\sim$10$\times$ reduction in area compared to a digital fixed-point MLP at similar approximation error. These results establish RNPUs as scalable, hardware-native nonlinear computing primitives and identify analog KAN architectures as a realistic silicon-based pathway toward energy-, latency-, and footprint-efficient analog neural-network hardware, particularly for edge inference.

Related papers

• Bridging Function Approximation and Device Physics via Negative Differential Resistance Networks [17.356504841908826]
  KANalogue is a fully analogue implementation of Kolmogorov-Arnold Networks (KANs) using negative differential resistance devices.<n>By leveraging the intrinsic negative differential resistance characteristics of tunnel diodes fabricated from NbSi2N4/HfSi2N4 heterostructures, we construct coordinate-wise nonlinearities.<n>Our results demonstrate that KANalogue can approximate complex functions with minimal parameters while maintaining classification accuracy competitive with digital baselines.
  arXiv  Detail & Related papers  (2025-10-24T15:38:22Z)
• CMOS Implementation of Field Programmable Spiking Neural Network for Hardware Reservoir Computing [0.0]
  Large-scale neural networks, such as Deep Neural Networks (DNNs) and Large Language Models (LLMs), challenge their practical deployment in edge applications due to high power consumption, area requirements, and privacy concerns.<n>This work presents a novel CMOS-implemented field-programmable neural network architecture for hardware reservoir computing.
  arXiv  Detail & Related papers  (2025-09-22T05:19:46Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors [2.812395851874055]
  Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads. We demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2.
  arXiv  Detail & Related papers  (2023-03-14T08:56:54Z)
• Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays [3.1543509940301946]
  We show the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware.
  arXiv  Detail & Related papers  (2022-12-09T07:28:45Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference [9.329021390526124]
  Non-linear operations such as GELU, Layer normalization, and Softmax are essential yet costly building blocks of Transformer models. This paper proposes an accurate and hardware-friendly approximation framework for efficient Transformer inference.
  arXiv  Detail & Related papers  (2021-12-03T23:06:57Z)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)
• Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks [52.32646357164739]
  We propose a deep neural network (DNN) to solve the solutions of the optimal power flow (ACOPF) The proposed SIDNN is compatible with a broad range of OPF schemes. It can be seamlessly integrated in other learning-to-OPF schemes.
  arXiv  Detail & Related papers  (2021-03-27T00:45:23Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• Training End-to-End Analog Neural Networks with Equilibrium Propagation [64.0476282000118]
  We introduce a principled method to train end-to-end analog neural networks by gradient descent. We show mathematically that a class of analog neural networks (called nonlinear resistive networks) are energy-based models. Our work can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
  arXiv  Detail & Related papers  (2020-06-02T23:38:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.