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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
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Aceleración de la inteligencia artificial fotónica universal

Sufi R Ahmed1, Reza Baghdadi1, Mikhail Bernadskiy1

  • 1Lightmatter, Mountain View, CA, USA.

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Este resumen es generado por máquina.

Los investigadores desarrollaron un procesador fotónico de IA capaz de ejecutar modelos complejos de IA con una precisión casi electrónica. Este avance hace avanzar la computación fotónica para aplicaciones de IA, ofreciendo un camino más allá de la electrónica tradicional.

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Área de la Ciencia:

  • La fotónica
  • Inteligencia artificial (IA)
  • Aprendizaje profundo
  • Ingeniería informática

Sus antecedentes:

  • La investigación fotónica se ha centrado en acelerar las operaciones tensoriales para la IA y el aprendizaje profundo para mejorar la eficiencia y el rendimiento energético.
  • El campo busca alternativas a la computación tradicional para sostener el progreso más allá de la Ley de Moore y la escala de Dennard.
  • Los chips fotónicos actuales carecen de la precisión para la IA práctica, con demostraciones limitadas a tareas básicas.

Objetivo del estudio:

  • Introducir un nuevo procesador fotónico de IA capaz de ejecutar modelos avanzados de IA.
  • Para demostrar el potencial de la computación fotónica para competir con los aceleradores electrónicos de IA.
  • Contribuir al desarrollo de tecnologías informáticas posteriores a los transistores.

Principales métodos:

  • Desarrollo de una nueva arquitectura fotónica de procesador de IA.
  • Ejecución de modelos avanzados de IA, incluidos ResNet y BERT, en el procesador fotónico.
  • Integración y prueba del algoritmo de aprendizaje de refuerzo profundo de Atari.

Principales resultados:

  • El procesador fotónico de IA ejecutó con éxito modelos complejos de IA y un algoritmo de aprendizaje de refuerzo profundo.
  • El procesador logró una precisión casi electrónica para una variedad de cargas de trabajo.
  • Esto marca un avance significativo en la computación fotónica para aplicaciones de IA.

Conclusiones:

  • El procesador fotónico de IA desarrollado demuestra capacidades prácticas para tareas avanzadas de IA.
  • La computación fotónica está emergiendo como un competidor viable para los aceleradores de IA electrónicos establecidos.
  • Este trabajo representa un paso crucial hacia futuros paradigmas de computación post-transistor.