VIKIN: A Reconfigurable Accelerator for KANs and MLPs with Two-Stage Sparsity Support

2026-03-01

AI Summary

This work addresses the challenges of efficiently deploying both Multilayer Perceptrons (MLPs) and the emerging Kolmogorov–Arnold Networks (KANs) on edge devices, where MLPs incur high memory overhead and KANs lack dedicated hardware support, compounded by their divergent compute and memory access patterns that hinder unified acceleration. To bridge this gap, we propose VIKIN, a reconfigurable accelerator that, for the first time, enables efficient unified support for both KANs and MLPs. VIKIN employs a hybrid execution paradigm—pipeline-based for KANs and parallel for MLPs—augmented with two-level sparsity optimizations to accommodate their distinct computational characteristics. Experimental results on real-world datasets show that VIKIN achieves a 1.28× speedup for KAN inference over MLPs with 19.58% lower accuracy loss; compared to an edge GPU, it delivers a 1.25× speedup and 4.87× higher energy efficiency for KAN workloads.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Multi-modal Vision
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, multi-layer perceptrons (MLPs) widely used in modern AI applications suffer from limited real-time performance due to intensive memory access overhead. Kolmogorov--Arnold Networks (KANs) have attracted increasing attention as an alternative architecture with similar structures to MLPs but improved parameter efficiency. However, the lack of dedicated hardware support limits the practical performance benefits of KANs. Moreover, since many edge workloads still rely heavily on MLPs, accelerators designed exclusively for KANs become inefficient and impractical. In this work, we present VIKIN, a reconfigurable accelerator that efficiently supports both KAN and MLP inference using unified hardware. VIKIN introduces a pipeline execution mode and two-stage sparsity support for efficient KAN processing, while enabling parallel-mode acceleration to improve MLP throughput under the same sparsity framework. Experiments on real-world datasets demonstrate that replacing MLPs with KANs on VIKIN achieves $1.28\times$ acceleration with $19.58\%$ reduced accuracy loss. For a higher-accuracy KAN model requiring $3.29\times$ more operations, VIKIN incurs only $1.24\times$ latency overhead compared with the baseline KAN model. In addition, VIKIN achieves $1.25\times$ speedup and $4.87\times$ higher energy efficiency than a representative edge GPU when executing KAN workloads.

Problem

• KANs
• MLPs
• reconfigurable accelerator
• sparsity
• edge computing

Innovation

• Reconfigurable Accelerator
• Kolmogorov–Arnold Networks
• Two-Stage Sparsity
• Unified Hardware Architecture
• Edge AI Inference