Performance Evaluation of Convolutional Networks on Heterogeneous Architectures for Applications in Autonomous Robotics

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dc.creatorGuajo, Joaquín
dc.creatorAlzate-Anzola, Cristian
dc.creatorCastaño-Londoño , Luis
dc.creatorMárquez-Viloria, David
dc.date2022-04-29
dc.date.accessioned2025-10-01T23:52:46Z
dc.descriptionHumanoid robots find application in human-robot interaction tasks. However, despite their capabilities, their sequential computing system limits the execution of computationally expensive algorithms such as convolutional neural networks, which have demonstrated good performance in recognition tasks. As an alternative to sequential computing units, Field-Programmable Gate Arrays and Graphics Processing Units have a high degree of parallelism and low power consumption. This study aims to improve the visual perception of a humanoid robot called NAO using these embedded systems running a convolutional neural network. The methodology adopted here is based on image acquisition and transmission using simulation software: Webots and Choreographe. In each embedded system, an object recognition stage is performed using commercial convolutional neural network acceleration frameworks. Xilinx® Ultra96™, Intel® Cyclone® V-SoC and NVIDIA® Jetson™ TX2 cards were used, and Tinier-YOLO, AlexNet, Inception-V1 and Inception V3 transfer-learning networks were executed. Real-time metrics were obtained when Inception V1, Inception V3 transfer-learning and AlexNet were run on the Ultra96 and Jetson TX2 cards, with frame rates between 28 and 30 frames per second. The results demonstrated that the use of these embedded systems and convolutional neural networks can provide humanoid robots such as NAO with greater visual recognition in tasks that require high accuracy and autonomy.en-US
dc.descriptionLos robots humanoides encuentran aplicación en tareas de interacción humano-robot. A pesar de sus capacidades, su sistema de computación secuencial limita la ejecución de algoritmos computacionalmente costosos, como las redes neuronales convolucionales, que han demostrado buen rendimiento en tareas de reconocimiento. Como alternativa a unidades de cómputo secuencial se encuentran los Field Programmable Gate Arrays y las Graphics Processing Unit, que tienen un alto grado de paralelismo y bajo consumo de energía. Este trabajo tuvo como objetivo mejorar la percepción visual del robot humanoide NAO utilizando estos sistemas embebidos que ejecutan una red neuronal convolucional. El trabajo se basó en la adquisición y transmisión de la imagen usando herramientas de simulación como Webots y Choreographe. Posteriormente, en cada sistema embebido, se realizó una etapa de reconocimiento del objeto utilizando frameworks de aceleración comerciales de redes neuronales convolucionales. Luego se utilizaron las tarjetas Xilinx Ultra96, Intel Cyclone V-SoC y Nvidia Jetson TX2; después fueron ejecutadas las redes Tinier-Yolo, Alexnet, Inception V1 y Inception V3 transfer-learning. Se obtuvieron métricas en tiempo real cuando Inception V1, Inception V3 transfer-learning y AlexNet fueron ejecutadas sobre la Ultra96 y Jetson TX2, teniendo como intervalo entre 28 y 30 cuadros por segundo. Los resultados demostraron que el uso de estos sistemas embebidos y redes neuronales convolucionales puede otorgarles a robots humanoides, como NAO, mayor reconocimiento visual en tareas que requieren alta precisión y autonomía.  es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2170
dc.identifier10.22430/22565337.2170
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7810
dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2170/2385
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dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2170/2388
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dc.rightsDerechos de autor 2022 TecnoLógicases-ES
dc.sourceTecnoLógicas; Vol. 25 No. 53 (2022); e2170en-US
dc.sourceTecnoLógicas; Vol. 25 Núm. 53 (2022); e2170es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectConvolutional neural networksen-US
dc.subjectfield programmable gate arrayen-US
dc.subjectsystem-on-a-chipen-US
dc.subjecthigh-level synthesisen-US
dc.subjecthumanoid roboten-US
dc.subjectRedes neuronales convolucionaleses-ES
dc.subjectmatriz de puertas lógicas programable en campoes-ES
dc.subjectsistema en chipes-ES
dc.subjectsíntesis de alto niveles-ES
dc.subjectrobot humanoidees-ES
dc.titlePerformance Evaluation of Convolutional Networks on Heterogeneous Architectures for Applications in Autonomous Roboticsen-US
dc.titleEvaluación de desempeño de redes convolucionales sobre arquitecturas heterogéneas para aplicaciones en robótica autónomaes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES

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