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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Cross Product01:25

Cross Product

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The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Vector Product (Cross Product)01:17

Vector Product (Cross Product)

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Vector multiplication of two vectors yields a vector product, with the magnitude equal to the product of the individual vectors multiplied by the sine of the angle between both the vectors and the direction perpendicular to both the individual vectors. As there are always two directions perpendicular to a given plane, one on each side, the direction of the vector product is governed by the right-hand thumb rule.
Consider the cross product of two vectors. Imagine rotating the first vector about...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Video Experimental Relacionado

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Redes neuronales gráficas con atención cruzada de configuración para compiladores de tensores

Dmitrii Khizbullin1, Eduardo Rocha de Andrade2, Thanh Hau Nguyen2

  • 1King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

Frontiers in artificial intelligence
|September 5, 2025
PubMed
Resumen

TGraph acelera la inferencia de la red neuronal mediante la selección inteligente de las configuraciones tensoriales. Este compilador de tensores de IA mejora significativamente el rendimiento y reduce el consumo de energía en los centros de datos de IA.

Palabras clave:
Mecanismo de atenciónred neuronal de gráficos (GNN)Aprendizaje automático para sistemasFunción de pérdida de clasificaciónCompilación de tensores

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Área de la Ciencia:

  • Inteligencia artificial
  • Ciencias de la computación
  • Aprendizaje automático

Sus antecedentes:

  • La inferencia de la red neuronal requiere un cálculo eficiente de las operaciones tensoriales.
  • La optimización de diseños de tensores (transposiciones, tilings) es combinatoriamente compleja.
  • Los compiladores existentes a menudo se basan en heurísticas, lo que limita las ganancias de rendimiento.

Objetivo del estudio:

  • Para introducir TGraph, una nueva arquitectura de gráficos neuronales para optimizar la inferencia de la red neuronal.
  • Desarrollar un compilador de tensores de IA capaz de detectar configuraciones de tensores rápidas.
  • Para reducir el costo computacional y la huella energética de la inferencia de IA.

Principales métodos:

  • Representando la inferencia de la red neuronal como un gráfico computacional.
  • Utilizando una arquitectura gráfica para explorar las permutaciones de diseño de tensores.
  • Desarrollo de un marco de compilación de tensores de IA distinto de los enfoques tradicionales.

Principales resultados:

  • TGraph mejora significativamente el τ promedio de Kendall en las colecciones de diseño, alcanzando el 67,4% en comparación con una línea de base del 29,8%.
  • El método propuesto actúa como un compilador de tensores de IA, superando a los métodos basados en heurísticas.
  • Reducción potencial estimada de las emisiones de CO2 equivalente a más del 50% de las emisiones de los hogares en las regiones de centros de datos de IA.

Conclusiones:

  • TGraph ofrece un nuevo y poderoso enfoque para optimizar la inferencia de la red neuronal.
  • El marco del compilador de tensores de IA demuestra mejoras sustanciales en el rendimiento.
  • Este trabajo tiene implicaciones significativas para la eficiencia energética en la inteligencia artificial.