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Arquitectura de computación en memoria para redes de Kolmogorov-Arnold basada en celdas de memoria sintonizables
Zhixing Wen1,2, Qirui Zhang1, Jiangang Chen1
1School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Nature communications
|February 20, 2026
Resumen
Los investigadores desarrollaron celdas de memoria similares a Gaussianas para redes eficientes de Kolmogorov-Arnold. Esta novedosa arquitectura de computación en memoria mejora la flexibilidad y la eficiencia energética en tareas de computación neuromórfica.
Área de la Ciencia:
- Ingeniería Neuromórfica
- Inteligencia Artificial
Sus antecedentes:
- Las redes de Kolmogorov-Arnold (KANs) ofrecen ventajas sobre los perceptrones multicapa tradicionales debido a sus funciones de activación flexibles.
- La implementación en hardware de las funciones base de KANs es computacionalmente costosa, lo que dificulta las aplicaciones prácticas.
Objetivo del estudio:
- Diseñar una arquitectura de hardware rentable para redes de Kolmogorov-Arnold.
- Mejorar la eficiencia energética y la flexibilidad de KANs para cálculos complejos.
Principales métodos:
- Se desarrolló una celda de memoria similar a Gaussiana que combina un transistor gaussiano y un memristor para obtener respuestas de corriente-voltaje sintonizables.
- Se construyeron circuitos utilizando estas celdas para permitir la computación de inferencia paralela para KANs.
Principales resultados:
- La arquitectura propuesta implementó con éxito KANs para diversas tareas como regresión de funciones, reconocimiento de imágenes y pronóstico de series temporales.
- Demostró mejoras significativas en la eficiencia energética en comparación con los métodos existentes.
- Validó las ventajas algorítmicas de KANs en el nuevo hardware.
Conclusiones:
- Las celdas de memoria similares a Gaussianas proporcionan una solución prometedora de computación en memoria para redes de Kolmogorov-Arnold.
- La arquitectura desarrollada mejora la flexibilidad y la eficiencia del paradigma de computación neuromórfica.
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