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Computación neuromórfica fotónica con memoria analógica electro-óptica

Sean Lam1, Ahmed Khaled2, Simon Bilodeau3

  • 1Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. seanlm@student.ubc.ca.

Nature communications
|February 7, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Desarrollamos una memoria electrónica analógica integrada con circuitos fotónicos neuromórficos para reducir los costos de energía. Esta innovación logra más de 26 veces de ahorro de energía para tareas de aprendizaje automático, permitiendo una computación eficiente y de alta velocidad.

Palabras clave:
computación neuromórfica fotónicamemoria analógicaahorro de energíaaprendizaje automáticohardware de IA

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Área de la Ciencia:

  • Ingeniería Neuromórfica
  • Computación Fotónica
  • Hardware de Aprendizaje Automático

Sus antecedentes:

  • Los sistemas fotónicos neuromórficos dependen de señales analógicas, lo que requiere convertidores digital-analógico (DAC) y analógico-digital (ADC) que consumen mucha energía.
  • Las arquitecturas convencionales de von Neumann enfrentan costos energéticos significativos debido al movimiento de datos entre la memoria y los convertidores.

Objetivo del estudio:

  • Proponer y demostrar una memoria electrónica analógica integrada directamente con unidades de computación fotónica.
  • Eliminar el consumo de energía del movimiento de datos y reducir la dependencia de DAC/ADC en sistemas fotónicos neuromórficos.

Principales métodos:

  • Integración monolítica de un circuito fotónico neuromórfico con memoria analógica capacitiva en chip.
  • Evaluación del rendimiento utilizando aprendizaje automático para entrenamiento e inferencia in situ en el conjunto de datos MNIST.

Principales resultados:

  • Se logró un ahorro de energía de más de 26 veces en comparación con las arquitecturas convencionales de SRAM-DAC.
  • Se demostró una precisión de inferencia >90% con una relación mínima de retención de memoria analógica a latencia de red de 100.
  • Se mostró la viabilidad de memorias analógicas con fugas sin una degradación sustancial del rendimiento.

Conclusiones:

  • La integración de memoria analógica en arquitecturas fotónicas neuromórficas ofrece una vía escalable hacia la computación energéticamente eficiente y de alta velocidad.
  • Este enfoque minimiza significativamente el movimiento de datos y la dependencia de los convertidores.
  • Permite la implementación práctica de sistemas fotónicos neuromórficos para tareas avanzadas de IA.