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Eficiencia comparativa en la ejecución de algoritmos de visión artificial

 <= /span>

Comparative efficiency in the execution of artificial vision algorithms

 

 

1

Oswaldo Martínez Guashima        &nbs= p;            &= nbsp;          https://orcid.org/0000-0001-9018-= 7777

Escuela Superior Politéc= nica de Chimborazo (ESPOCH), Facultad de Informática y Electróni= ca. Riobamba-Ecuador

o.martinez@espoch.edu.e= c

2

Jorge Paucar Samaniego             &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;      https://orcid.org/0000-0002-1704-= 8583

Escuela Superior Politécnica de Chimborazo (ESPOCH), Facultad de Informática y Electrónica. Riob= amba-Ecuador

jlpaucar@espoch.edu.ec

3

Christian Alberto Costales Espinoza       &nbs= p;             https://orcid.org/0000-0001-8021-1489

Univ= ersidad Estatal de Bolívar (UEB). Guaranda-Ecuador

ccostales@ueb.edu.ec

4

Christian Fernando Barragán Quizhpe       &nbs= p;           https://orcid.org/00= 00-0003-4699-9553

Univ= ersidad Estatal de Bolívar (UEB). Guaranda-Ecuador

cbarragan@ueb.edu.ec

 

 <= /span>

 

 

 

 

 

 

 

 

 

 

 

 

=  

=  



 

Artículo = de Investigación Científica y Tecnológica

Enviado: 12/05/2= 022

Revisado: 27/06/= 2022

Aceptado: 15/07/= 2022

Publicado:05/08/= 2022

DOI:  &= nbsp;    https://doi.org/10.33262/= ap.v4i3.1.241        

 

 

 

 

Cítese:

 

 

Martínez Guashima, O., Paucar Samaniego, = J., Costales Espinoza, C. A., & Barragán Quizhpe, C. F. (2022). Eficiencia comparativa en la ejecución de algoritmos de visi&oacut= e;n artificial. AlfaPublicaciones, 4(3.1), 109–126. https://doi.org/10.33262/= ap.v4i3.1.241

 <= /span>

 

 

ALFA PUBLICACION= ES, es una Revista Multidisciplinar, Trimestral, que se publicará en soporte electrónico tiene como misión contribuir a la   formación de  profesionales competentes con visión humanística y crítica que sean capaces de&nbs= p; exponer  sus resultados  investigativos y científicos en= la misma medida que se promueva mediante su intervención cambios positivos en  la sociedad. https://alfapublicaciones.com=  

3Deditorial1.pngLa revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registra= da en la Cámara Ecuatoriana de Libro con No de Afiliación 663)= <= span style=3D'font-size:8.0pt;line-height:115%;font-family:"Times New Roman",s= erif; mso-fareast-font-family:Calibri;color:#0563C1'>www.celibro.org.ec<= /a>

 

 

 

Esta revista está protegida bajo una licencia Creative Commons AttributionNonCommercialNoDerivatives 4.0 International. Copia de la licencia: http://creativecommons.org= /licenses/by-nc-nd/4.0/

 

Palabras claves:= aceleradores de redes neuronales, raspbe= rry pi 4, redes neuronales, visión artificial.

 

 

Resumen

Introducció= n: Una red neuronal es un algoritmo programado con = base en un modelo de inferencia Para la ejecución de un proceso de visión es necesario una inferencia avanzada también es dese= able un bajo consumo de energía Objetivos: analizar la eficiencia comparativa en la ejecución de visión artificial mediante sistemas embebidos. Metodolog= ía: Comparar los tiempos de inferencia que conlleva el ejecutar una red neuronal profunda en un sistema embebido del tipo Raspberry Pi 4 y un acelerador de inferencias diseñado por Intel Resultados: Se detalla el proceso de instalación y configuración necesaria para la compatibilidad entre los dos dispositivos, se utiliza modelos de redes neuronales ya entrenadas enfocadas al procesamiento= de imágenes y se compara el tiempo de inferencia que conlleva el ejecutarlos Conclusiones: Al final se llega a la conclusión que para estas condiciones experimentales el tiempo de inferencia se ha mejorado en un 25 %.

 

 

Keywords: neural network accelerators, raspberr= y pi 4, neural networks, artificial vision.

 

= Abstract

Introduction: A neural network is a programmed algorithm based on an inference model. For the execution of a vision process, advan= ced inference is necessary. Low energy consumption is also desirable. Obje= ctives: to analyze the comparative efficiency in the execution of artificial visi= on through embedded systems. Methodology: To compare the inference ti= mes involved in executing a deep neural network in an embedded system such as Raspberry Pi 4 and an inference accelerator designed by Intel. Results= : The installation and configuration process necessary for compatibility between the two devices is detailed already trained neural network models focused on image processing are used and the inference time involved in e= xecuting them is compared. Conclusions: In the end, it is concluded that for these experimental conditions the inference time has been improved by 25%= .

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Introducción

Una r= ed neuronal es un algoritmo progr= amado con base en un modelo de inferencia cuyo objetivo es imitar el funcionamiento de un sistema neuronal humano y que puede interactuar con otras similares intercambiando informaci&= oacute;n con el fin de cumplir un objetivo y representarlo en salidas continuas o discretas.

Una de las aplicaciones más comunes es aque= llas en las cuales interviene el procesamiento de imágenes ejecutadas en sistemas micro procesados. La visión por computador permite adquirir= una imagen usando dispositivos electrónicos para procesarla y obtener un resultado en específico como por ejemplo una clasificación. T= odo este proceso conlleva un con- junto de etapas que requieren un procesamiento computacional elevado.

Para la ejecución de un proceso de visión es necesario una inferencia avanzada también es deseab= le un bajo consumo de energía en el hardware y un rendimiento elevado, sin embargo, la mayoría de los dispositivos poseen un nivel de procesamiento limitado. Para redes neuronales simples estas limitaciones no influyen en sus resultados, pero, para sistemas complejos de inteligencia artificial, se necesita dispositivos de coprocesamiento. En los últimos años mucha= s empresas han desarrollado dispositivos dedicados a procesar programas de inteligencia artificial, empresas como Google, Intel o Nvidia poseen en= su portafolio de productos dispositivos que reducen los altos requerimientos de procesamiento.=

Estad= o del arte

En est= a sección se analiza la información que permita identificar los requerimientos de un sistema de inteligencia artificial enfo= cado a procesos de visión, para lo cual se estudia los c= onceptos y teorías útiles para el desarrollo.

Redes Neuronales

Una red neuronal asimila información de for= ma jerarquizada y por fases, esta se define en función del númer= o de capas que contenga la red siendo las primeras aquellas que aprenden concept= os muy concretos y las capas subsiguientes las que aprenden conceptos abstract= os. El proceso de aprendizaje de una red neuronal se establece en dos fases que comprenden (Vázquez, = 2020):

1)&n= bsp;     Fase de entrenamiento: en la cual ingresa la información etiquet= ada con el fin de construir el modelo, a su vez necesita una cantidad de datos considerable que permitan establecer características de entrada y salida.

= 2)      Fase de inferencia: una vez que el modelo ha sido desarrollado y se han establecido etiquetas se debe realizar prediccio= nes de los datos no etiquetados.

Arquitectura de una red neuronal

La arq= uitectura básica, con una entrada y una salida, de una red neuronal se puede divisar en la figura 1, en esta se encuentra la ubicación de la entrada X1= , la salida y, el bías b y su peso w. (Rengifo, 2002).

Figura 1=

Arquitectura neurona artificial

 

Función de activación: Como menciona Garduño & Padrón (2000), una red neuronal artificial se puede aproximar de manera matem&a= acute;tica a una función lineal o a una no lineal. Para las aproximacio= nes no lineales se utiliza funciones de activación que permiten disminuir la complejidad matemática en el sistema, lo que implica que la salida de la red está es la función de la ecuación 1.

y =3D f uncion_activacion(x)               &= nbsp;   (1)

Taza de aprendizaje: Es necesario considerar un parámetro que influye en la modificación de los pesos w y el bías b del modelo, se lo conoce como learning rate su símbolo es η y determina la velocidad con la que se modifica sus valores y el error (Jiménez, 2012). Este parámetro es esencial en redes que se entrenan con backpropagation = el cual se considera una herramienta para disminuir el error generado ya que la relación entre estos parámetros es directamente proporcional c= omo se define en la ecuación 2.

∆ω =3D ω (t + 1) − ω(t) =3D η · error  = ;        (2)

Algoritmo backpropagation: En el entrenamiento de<= span style=3D'letter-spacing:.05pt'> una red neuronal se pueden utilizar varios algoritmos que,<= span style=3D'letter-spacing:-.4pt'> dependiendo de la aplicación, pueden modific= ar su eficiencia (Matich, 2001). Entre estos algoritmos se encuentra el denominado<= span style=3D'letter-spacing:-.05pt'> como backpropagation que consiste en una propagación de errores hacia atrás, usando el valor del error generado en la salida de una red neuronal artificial multicapa para ajustar los parámetros que involucran la respuesta a una entrada, esto genera que se reduzca el error en cada interacción (Juárez & Pascual= , 2015).

Redes neuronal convolucionales

Una CNN es una arquitectura de uso común en procesos de aprendizaje profundo, se usa en sistemas de visión artificial cuya característica principal de su configuración multicapa permiten procesar imágenes de entrada y obtener, con mayor detalle, sus características (Ordoñez, 2020).

El proceso de convolución consiste en proce= sar imágenes aplicando filtros, conocidos también como kernels, específicos con el fin de extraer características, varias máscaras producirán resultados diferentes debido a una combinación lineal de los pixeles de entrada (Espinola et al., 2011).

Hardware para= IA

En Gómez & Vélez (2= 015), se detalla que en aplicaciones de= inteligencia artificial es necesario encontrar el hardware correcto que permita la ejecución de los algoritmos de manera eficiente. Es importante considerar parámetros como la capacidad de almacenamiento de los sistemas microprocesados, su velocidad de procesamiento y su capacidad de realizar procesamiento en paralelo tomando en cuenta que se ejecutará inferencias complejas por lo cual no se debe sacrificar precisión en= sus resultados.

Aceleradores: En el artículo presentado por Montoya (2020), se detalla que un acelerador de inteligencia artif= icial es un dispositivo embebido dedicado a ejecutar aplicaciones de redes neuronales, visión artificial y aprendizaje automático. Las aplicaciones consisten en la ejecución de algoritmos complejos usando una gran cantidad de datos o el barrido de dispositivos externos, como sens= ores, que requieren una elevada capacidad de cálculo con procesamiento en paralelo. Las CPU tradicionales pueden ser utilizadas para este propósito, sin embargo, para la ejecución de algoritmos de inteligencia artificial se requiere cálculos concretos durante la fa= se de aprendizaje e inferencia con operaciones de matrices muy elaboradas. Una= CPU convencional realiza este tipo de operaciones en función del número de núcleos que posee con una limitación importa= nte que es la ejecución de 4 cálculos por ciclo de máquina (CM) acortando así el número de operaciones.

Intel Stick: Es un dispositivo USB que posee una Intel Neural Computer Stick 2 (Intel Movidius Myriad X), que es considerado un motor de cálculo neuronal el cual se aplica en inferencias de<= span style=3D'letter-spacing:-.35pt'> redes neuronales profundas dedicadas a procesos de visión artificial. Posee= 16 núcleos con una arquitectura VLIW (Very Long Instruction Word lo que ayuda en la ejecución de procesos en paralelo (Fernández, 2020).

No nece= sita conectarse a la internet ya que se ejecuta la inferencia en tiempo real y su consumo de energía es bajo lo que permite operar con sistemas como la Raspberry Pi. Soporta frameworks como TensorFlow, Caffe, Apache MxNet, Pytorch con una conectividad USB 3.0, como se puede observar en la figura 2.

 

 

Figura 2

FrameWorks soportados

=

 

 

 

 

 

Fuente: Intel.com (2022)

 

El fabricante ha desarrollado<= span style=3D'letter-spacing:2.3pt'> un kit de herramientas denominado OpenVINO, este<= span style=3D'letter-spacing:1.1pt'> permite optimizar aplicaciones de deep learning en CNN, el software posee soporte para los dispositivos del tipo Neural Computer Stick (Rodrígu= ez & Ruz, 2020), tambi&= eacute;n incluye librerías en funciones de visió= n artificial y filtros pre optimizados lo que disminuye el tiempo de desarrollo de las aplicaciones e incrementa el rendimiento de los procesadores utilizando un optimizador de modelos y un motor de inferencia.: El optimizador de modelos permite una transición entre los entornos de entrenamiento y despliegue de una red neuronal, ajusta los modelos de deep learning y genera un análisis estático creando una representación intermedia del modelo ya entrenado que puede ser inferida en su motor para esto necesita dos archivos fundamentales (Salazar, 2017).=

1)&n= bsp;     Archivo .xml = en el cual se describe la topología de la red.

= 2)      Archivo .bin = el cual contiene los pesos y los datos binarios de la red.

Una vez que el optimizador de modelos ha logrado su objetivo el motor de inferencia se encarga de obtener resultados en función de los datos de entrada. Su funcionamiento se basa en una arquitectura de plugins lo cual permite su compatibilidad con hardware de intel, es decir el motor de inferencia puede ser ejecutado en CPU, FPGA o MYRIAD de intel.

Hardware embebido compatible: Es necesario conectar el acelerador de hardware a un sistema microprocesado (em- bebido) usando un puerto de comunicaciones del tipo serial (USB) con el fin de acelerar la inferencia. Uno de estos dispositivos es la Raspberry Pi que es una computadora ensamblada en una sola tarjeta o circuito que, dependiendo del modelo, puede tener características = de procesamiento específicas. En este caso se utiliza un modelo Pi 4 como el de la figura 3 con particularidades básicas como:

 

 

 

Figura 3=

Raspberry Pi 4

        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;  Fuente: Raspberry Pi. (2022)

 

Procesador: =  Broadcom BCM2711, Quad Core Cortex A72, arquitectura de 64 bits a 1.5 Ghz.

Ram de= 4GB LPDD4-3200

Metodo= log<= /b>ía

Para Evaluar el tiempo de inferencia que conlleva el ejecutar un algoritmo de inteligencia artificial sobre dos tipos de hardware diferente, uno de ellos utiliza un coprocesador del tipo Intel Neural Computer Stick 2. es necesario ejec= utar los algoritmos en funció= n del procedimiento descrito en la figura 4.

Se op= ta por utilizar dos IDEs diferentes el primero Jupyter y el segundo la consola de Python de la Raspberry Pi 4.

Modelo por utilizar y target: Es necesario importar las librerías necesarias para manipular<= span style=3D'letter-spacing:1.35pt'> la información. Se importan librerías como openCV simbolizado por cv2, se requiere escribir y leer archivos que contienen información para esto se utiliza os, numpy es necesario para manipular vectores y matrices, la librería= time permite medir el tiempo de inferencia del algoritmo y por último PIL que permitirá el tratamiento<= span style=3D'letter-spacing:1.35pt'> y edición de imágenes.

El mo= delo se importa desde una ubicación específica con los archivos de terminación .xml y .bin para el motor de inferencia OpenVINO. Se debe especificar que es el único formato permitido en la Intel Neural Computer Stick 2, la selección del target depende del hardware como se puede observar en la figura 5.

Imágenes: Se necesita establecer la dirección o pat= h en la cual se almacenan las imágenes que ingresan a la inferencia del modelo. Es necesario unificar el tamaño de las imágenes con el fin de limitar el número de píxeles de entrada estandarizando= así su tamaño.

 

 

Figura 4=

Procesamiento de inferenci= a

 

Figura 5=

Backends= y targets de OpenCV

             =             &nb= sp;            =          

Ejecución de la inferencia: Con base en el modelo se desarrolla el código= de inferencia en el cual se ingresa la imagen acondicionada antes de la inferencia, se inicia un contador de tiempo que se detendrá luego de obtener el resultado. Para finalizar se genera una variable que permite calcular el tiempo promedio de la clasificación <= /span>de todas las imágenes que contiene la carpeta utilizando la herramienta d= e valor medio, mínimo y máximo del conjunto de valores almacenados en el arreglo.

Resultados

Una vez implementados los algoritmos de inferencia sobre los dos tipos de hardware se presenta los resultados obtenidos.

odelo Intel

El modelo utilizado es proporcionado por Intel par= a la inferencia sobre una Intel Neural Computer Stick 2 y un CPU. La métr= ica para la detección de personas es del 73.63%, el número de obj= etos máximos de detección son 200 y el número de operacione= s de coma flotante es de 3560 Gflops. El modelo está entrenado en función de 219181 imágenes y para su inferencia necesita una entrada de las características:

name: input.1, shape: [1x3x512x512] - An input ima= ge in the format [BxCxHxW].

B batch size

C número de canales.

H altura de la imagen.

W ancho de la imagen.

Raspberry Pi 4: Los resultados de los tiempos de inferencia del algoritmo implementado en el hardware Raspberry Pi 4 se puede observar en la figura 6.

Figura 6

Tiempos de inferencia

Las muestras del proceso de inferencia se observan= en las figuras 7 que es la imagen de entrada, la figura 8 que es la imagen de salida, cabe recalcar que en esta figura no se ha detectado todas las perso= nas.

Figura 7

Muestra = imagen de entrada

             =             &nb= sp;            =             

Figura 8

Muestra= imagen inferida en Raspberry Pi 4

             =             &nb= sp;            =            

Raspberry Pi 4 e Intel Neural Stick 2: Los resultados de los tiempos de inferencia del algoritmo implementado en el hardware Raspberry pi 4 más Intel= Neural Computer Stick 2 intel se puede observar en la figura 9 y la imagen resultante se observa en la figura 10 (Abellán, 2021).

 

 

 

 

 

Figura 9

Tiempos de inferencia Raspberry Pi 4 e Intel Neural Stick 2

Modelo = desarrollado

Esta = sección describe el proceso ejecutado con el fin de comparar<= span style=3D'letter-spacing:.9pt'> el tiempo de inferencia entre los dos tipos de hardware ya definidos partiendo de un modelo que se basa en openCV para la detección de rostros en una imagen. Este modelo requiere ser acondicionado con el fin de que se ejecute en la Intel Neural Computer Stick 2. Para facilitar la construcción,<= span style=3D'letter-spacing:.35pt'> entrenamiento e implementaci&o= acute;n de modelos de topologías comunes como SSD, FAST, CNN o RNN el motor de inferencia OpenVINO se adapta a cualquiera de estas, sin embargo, cuando se pretende ejecutar la inferencia en tiempo real es necesario que el algoritmo est&e= acute; realizado de manera óptima para que la Intel Neural Computer Stick 2 lo ejecute.

 

Figura 10

Muestra = imagen inferida Raspberry Pi 4 e Intel Neural Stick 2

             =             &nb= sp;            =          

Intel Movidi= us acelera la inferencia de redes = neuronales y gráficos directos, su ventaja radica en la etapa de preprocesamiento y post procesamiento que reemplaza = bucles de programación por capas en los niveles superiores de la red.

La her= ramienta que permite el acondicionamiento se denomina Model Optimizer que permite introducir modelos de- sarrollados, por ejemplo, en TensorFlow u OpenCV y permite obtener un modelo útil con extensión .xml y .bin que pueda ejecutarse, el proceso se puede divisar en la figura 11.

Figura 11

Proceso = de conversión del modelo

             =             &nb= sp;                

La fig= ura 12 representa una muestra de las imágenes de entrada, la<= span style=3D'letter-spacing:2.3pt'> figura 13 es la imagen de salida luego de ser ejecutado el algoritmo en la Raspberry 4 la cual tomó 0.22 segundos en su ejecución.<= /p>

Figura 12

Imagen= original

Fuente: <= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; mso-fareast-font-family:Arial;mso-ansi-language:ES-EC'>Elaboración propia

 

Raspberry Pi 4= e Intel Neural Stick 2: Es necesario acondicionar el modelo para ser ejecutado con la Intel Neural Computer Stick 2, para esto el código para ejecutar la inferencia se lo desarrolla en la consola de Python. Se puede distinguir en la figura 14 el resultado. El tiempo de inferencia fue de segundos.

Análisis de resultados

En est= a sección se analiza los resultados obtenidos al medir el tiempo de inferencia en los dos algoritmos.  = ;Una vez ejecutados sobre las dos combinaciones de hardware antes detalladas se establece dos consideraciones generales:

 

 

 

 

Figura 13

Imagen <= /span>de muestra resultante Raspberry Pi 4

             =             &nb= sp;                

 

Figura 14

Imagen = de muestra resultante Raspberry Pi 4 e Intel Neural Stick 2

             =             &nb= sp;                

1)&n= bsp;     Se toma en cue= nta el tiempo medio de clasificación de la Raspberry Pi 4 y la Raspberry= Pi 4 con Intel Neural Computer Stick 2.

= 2)      Se aplica una prueba t para medias de dos muestras emparejadas cuya variable uno son los tiempos de inferencia de cada una de = las imágenes en la Rapsberry Pi 4 y la variable dos es el tiempo de inferencia en la Raspeberry Pi 4 más la Intel Neural Stick 2.

El análisis estadístico de los resultados parte al establecer los dos posibles escenarios el primero que el dispositivo Raspberry Pi 4 ejecute en menor tiempo la clasificación = de imágenes que la Raspberry con Intel Neural Stick, para esto se fija = esa hipótesis como hipótesis nula Ho. Los resultados se pueden observar en el cuadro I.

Con un valor en el parámetro α del 5% = la diferencia promedio de los pesos, luego de modificar el hardware, es mayor a cero. Es decir que los tiempos de ejecución del algoritmo de inferen= cia se reducen en el segundo experimento, por lo tanto, se rechaza la hipótesis nula.

Como se puede divisar en el cuadro II y en la figu= ra 15 los tiempos de respuesta en la Raspberri Pi 4 son más lentos debi= do al rango intercuartil menor, por lo tanto, el tiempo de respuesta del siste= ma embebido es menor si no está trabajando en paralelo con el Intel Movidius Neural Compute Stick 2, lo cual le ha permitido alcanzar una mejora del 25%.

Tabla 1

Estadística de prue= ba T

 

RASPBERRY PI 4

RASPBERRY PI 4 + INTEL NEURAL STICK 2

Media

0,3558125

0,2885625

Varianza

0,004021763

5,28E-05

Observaciones

16

16

Coeficiente de correlación de Pearson

0,061298007

 

Diferencia hipotética de las medias

0

 

Grados de libertad

15

 

Estadístico t

4,243690582

 

P(T<=3Dt) una cola

0,000353837

 

Valor crítico de t (unas colas)

1,753050356

 

P(T<=3Dt) dos colas

0,000707674

 

Valor crítico de t (dos colas)

2,131449546

 

             =                

Figura 15

Tiempos de inferencia para un grupo de imágenes=

abla 2<= /b>

Estadís= tic= a descriptiva de los = tiempos de inferencia

 

RASPBERRY PI 4

RASPBERRY PI 4 + INTEL NEURAL STICK 2

Media

0,3558125

0,2885625

Error típico

0,01585434

0,001816519

Mediana

0,342

0,289

Moda

0,299

0,289

Desviación estándar

0,06341737

0,007266074

Varianza de la muestra

0,00402176

5,28E-05

Curtosis

-0,93536068

-0,656066026

Coeficiente de asimetría

0,74887509

0,11173525

Rango

0,173

0,024

Mínimo

0,289

0,278

Máximo

0,462

0,302

Suma

5,693

4,617

Cuenta

16

16

 

             =               

Coclioe

 

·         La primera conclusión gira en torno a los procesos que se llevaron a cabo con el fin de instalar y configurar de manera óptima la Intel Neural Computer Stick 2 de Intel que, al ser una versión nueva, la información disponible por el fabricante era poco útil. También es importante considerar que, en la mayoría de las investigaciones, la combinación de estos dos dispositivos se utilice una Raspberry pi 3 por lo tanto toda la información disponible estaba enfocada a versiones anteriores lo que dificultaba la instalaci&oac= ute;n de librerías fundamentales para que el motor de inferencia funcione.

·         Los algoritmos de visión= utilizados en este estudio fueron dos elementos fundamentales a considerar, ya que el motor de inferencia era compatible solo con los backends comunes. El motor de inferencia ofrece una herramienta que permite acondicionar modelos entrenados con otros procedimientos y

·         optimizarlos para la ejecución en el acelerador. Es importante entender que el proceso de optimización se enfoca en convertir bucles cíc= licos en capas de inferencia en la etapa de preprocesamiento.

·         Bajo estas condiciones de experimento el porcentaje de tiempo que se reduce en las inferencias aproximadamente es del 25 %, mejorando los tiempos de ejecución. Se debe considerar evaluar el acelerador de Intel con una fuente de imágenes en tiempo real.

 

Referencias Bibliográficas

Abellán Campos, M. (2021). Implementación y aceleración de algoritmos de IA sobre Raspberry Pi.

Espinola Gonzales, J. E., Asís López, M. E., & Rod= ríguez Sabino, V. G. (2011). Sistema de visión artificial para la detección de somnolencia de conductores, basado en el comportamiento ocular.

Fernández de la Torre, A. (2020). Portabilidad y optimización de una red neuronal para la detección ráp= ida de daños en terremotos usando el toolkit OpenVINO.=

Garduño, J. I., & Padrón, A. (2000). Anális= is de Antenas Lineales Empleando Redes Neuronales Artificiales. In X Cong= reso Internacional de Electrónica, Comunicaciones y Computadoras, CONIELECOMP-2000, Cholula-Puebla, México (pp. 393-397).

Gómez Muñoz, L. F., & Vélez Gutiérre= z, J. D. (2015). Simulación del modelo de interacción de actores= del sistema de abastecimiento alimentario de Bogotá: implementació= ;n de tecnologías de modelado basado en agentes y metodología de sistemas suaves.

Intel.com. (2022). Inferencia de aprendizaje profundo de alto rendimiento. Intel.com. https://www.intel.com/content/www/us/en/developer/tools/openvino-tool= kit/overview.html.

Jiménez, M. F. (2012). Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería (Doctoral dissertation, Universitat Politècnica de València).

Juárez, G., & Pascual, M. (2015). Implementación de redes neuronales sobre lógica reconfigurable. Repositorio Nacio= nal Conacyt.

Matich, D. J. (2001). Redes Neuronales: Conceptos básicos y aplicaciones. Universidad Tecnológica Nacional, México, 41, 12-16.

Montoya Vásquez, D. A. (2020). Implementación y evaluación del rendimiento de redes neuronales densas en FPGA para la inferencia rápida, aplicadas a problemas en física y visión artificial.

Ordoñez Ramos, E. (2020). Deep Learning para la visión artificial e identificación del personal administrativo y docente de= la Universidad Nacional Micaela Bastidas de Apurímac 2018.

Raspber= ry Pi. (2022). Computing for everybody. raspberrypi.com. https://www.raspberrypi.com= /

Rengifo Mora, F. A. (2002). Determinación de fases sísmicas PN usando redes neuronales.

Rodríguez, S. A., & Ruz Gómez, R. (2020). Evaluación de algoritmos de machine learning para conducción.=

Salazar Vásquez, F. A. (2017). Implementación de un algoritmo de aprendizaje para la arquitectura Deep-Networks.

Vázquez Valle, G. (2020). Diseño de un sistema basado = en redes neuronales para la detección de tumores cerebrales mediante imágenes hiperespectrales.

3Dedito=

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento de la Revista Alfa Pub= licaciones.

 =

3Dedito=

El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autorizado= por el director de la Revista Alfa Publicaciones.

 

3D"logo_catalogo3b.jpg"
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        =             &nb= sp;            =      Científico e Industrial<= span style=3D'mso-spacerun:yes'>        =       Página 109 | 1

 

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