Co-Optimization of Analog Kolmogorov-Arnold Networks for Low-Power Function Approximation in Flexible Electronics

2026-06-26

AI Summary

This work addresses the challenges of efficiently implementing complex multivariate functions in flexible electronics, which are constrained by circuit density, power consumption, and non-idealities. For the first time, the Kolmogorov–Arnold representation theorem is introduced into flexible analog circuits, leading to the proposal of Analog Kolmogorov–Arnold Networks (AKANs). A co-optimization framework integrating circuit-level error modeling, spline parameter regularization, and hardware-software joint pruning enables significant reductions in hardware overhead while maintaining or even improving approximation accuracy. Experimental results across multiple benchmarks demonstrate that the proposed approach achieves average savings of approximately 30% in both area and power consumption, with peak reductions reaching 55% and 50%, respectively. The method exhibits strong robustness, generality, and energy efficiency, offering a promising pathway for resource-constrained flexible electronic systems.

Abstract

Wearable devices and Internet of Things (IoT) sensors require on-sensor processing of biosignals and environmental data, including computationally demanding operations such as nonlinear activation functions for neural network inference, sensor calibration curves to map raw readings to physical units, and signal preprocessing functions like logarithmic compression and power operations for feature extraction. These functions exhibit significant complexity, often involving transcendental operations and multivariate dependencies that are costly to implement digitally. Analog function approximation provides a power-efficient alternative by performing these computations in the analog domain, thereby reducing the energy overhead associated with analog-to-digital conversion and subsequent digital processing. Flexible Electronics (FE) present a particularly attractive platform for wearable applications due to mechanical flexibility and low-cost fabrication, but impose strict constraints on circuit density and power consumption, making efficient analog implementations critical but challenging. This work introduces Analog Kolmogorov-Arnold Networks (AKANs), developed via hardware-software co-optimization, to approximate these complex multivariate functions accurately under hardware imperfections. Our method incorporates circuit-level error modeling during training and applies pruning at both software and hardware levels to reduce area and power. Validation across multiple benchmarks demonstrates that our proposed pruning methodology not only reduces hardware cost but can also improve approximation accuracy by regularizing spline parameters. Results show up to 55% area and 50% power savings, with average reductions of nearly 30% across datasets, highlighting AKANs as a robust and generalizable framework for low-power analog function approximation in FE.

Problem

• analog function approximation
• flexible electronics
• low-power computing
• multivariate functions
• wearable devices

Innovation

• Analog Kolmogorov-Arnold Networks
• hardware-software co-optimization
• analog function approximation
• pruning
• flexible electronics