{"id":10246,"date":"2026-05-13T17:58:59","date_gmt":"2026-05-13T09:58:59","guid":{"rendered":"https:\/\/ieeker.com\/?p=10246"},"modified":"2026-05-13T18:11:39","modified_gmt":"2026-05-13T10:11:39","slug":"rk3588-npu-performance-industrial-edge-ai","status":"publish","type":"post","link":"https:\/\/ieeker.com\/es\/rk3588-npu-performance-industrial-edge-ai\/","title":{"rendered":"Rendimiento de la NPU RK3588: Qu\u00e9 significan realmente 6 TOPS para su proyecto de IA industrial"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"10246\" class=\"elementor elementor-10246\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a9acf82 e-flex e-con-boxed e-con e-parent\" data-id=\"a9acf82\" data-element_type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9633fe4 elementor-widget elementor-widget-text-editor\" data-id=\"9633fe4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-size: 14pt;\"><b data-path-to-node=\"1\" data-index-in-node=\"0\">Rendimiento de la NPU RK3588<\/b> determina si su dispositivo de borde puede manejar la inferencia de IA en tiempo real sin una GPU dedicada. Para la mayor\u00eda de las tareas de visi\u00f3n industrial, su NPU de 6 TOPS ofrece m\u00e1s de 50 FPS para YOLOv5s y sustituye eficazmente a los m\u00f3dulos b\u00e1sicos NVIDIA Jetson al equilibrar el coste, la potencia y la integraci\u00f3n de E\/S.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da6db4a elementor-widget elementor-widget-text-editor\" data-id=\"da6db4a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"3\">Principales conclusiones<\/h2><ul data-path-to-node=\"4\"><li><p data-path-to-node=\"4,0,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"4,0,0\" data-index-in-node=\"0\">Objetivo de rendimiento:<\/b> RK3588 alcanza 54+ FPS en YOLOv5s (INT8) y 244 FPS en ResNet18.<\/span><\/p><\/li><li><p data-path-to-node=\"4,1,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"4,1,0\" data-index-in-node=\"0\">La precisi\u00f3n importa:<\/b> 6 TOPS representan el rendimiento m\u00e1ximo de INT8; el rendimiento de FP16 es significativamente inferior (~0,5 TFLOPS).<\/span><\/p><\/li><li><p data-path-to-node=\"4,2,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"4,2,0\" data-index-in-node=\"0\">Flujo de trabajo:<\/b> Requiere <a class=\"ng-star-inserted\" href=\"https:\/\/github.com\/rockchip-linux\/rknn-toolkit2\" target=\"_blank\" rel=\"noopener\" data-hveid=\"4\">Kit de herramientas RKNN2<\/a> para la conversi\u00f3n y cuantificaci\u00f3n de modelos.<\/span><\/p><\/li><li><p data-path-to-node=\"4,3,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"4,3,0\" data-index-in-node=\"0\">Rentabilidad:<\/b> Ofrece un enfoque de SoC consolidado (CPU\/GPU\/NPU\/ISP) que reduce la lista de materiales total en comparaci\u00f3n con los aceleradores de IA discretos.<\/span><\/p><\/li><li><p data-path-to-node=\"4,4,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"4,4,0\" data-index-in-node=\"0\">Ajuste industrial:<\/b> Ideal para potencias inferiores a 10 W en las que se requiere un procesamiento multic\u00e1mara.<\/span><\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a190fa elementor-widget elementor-widget-text-editor\" data-id=\"4a190fa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"6\">\u00bfQu\u00e9 significa realmente \"rendimiento de la NPU RK3588\"?<\/h2><p data-path-to-node=\"7\"><span style=\"font-size: 14pt;\">Al evaluar <b data-path-to-node=\"7\" data-index-in-node=\"16\">Rendimiento de la NPU RK3588<\/b>Sin embargo, es crucial distinguir entre las cifras \"pico\" del marketing y la realidad del despliegue. En el mundo del edge computing, <a class=\"ng-star-inserted\" href=\"https:\/\/en.wikipedia.org\/wiki\/Instructions_per_second\" target=\"_blank\" rel=\"noopener\" data-hveid=\"5\">TOPS (Tera Operaciones por Segundo)<\/a> es la m\u00e9trica est\u00e1ndar. Sin embargo, los 6 TOPS del RK3588 est\u00e1n optimizados espec\u00edficamente para operaciones INT8 (enteros de 8 bits), habituales en la inferencia de aprendizaje profundo.<\/span><\/p><p data-path-to-node=\"8\"><span style=\"font-size: 14pt;\">Si su proyecto requiere operaciones matem\u00e1ticas de coma flotante de alta precisi\u00f3n (FP32), la NPU no es la herramienta adecuada: tendr\u00eda que recurrir a la CPU o la GPU, donde el rendimiento disminuye dr\u00e1sticamente. Para la IA industrial, el objetivo es casi siempre <b data-path-to-node=\"8\" data-index-in-node=\"219\">cuantificaci\u00f3n<\/b>: conversi\u00f3n de modelos a INT8 para aprovechar los 6 TOPS completos. Seg\u00fan <a class=\"ng-star-inserted\" href=\"https:\/\/en.wikipedia.org\/wiki\/List_of_Rockchip_products\" target=\"_blank\" rel=\"noopener\" data-hveid=\"6\">Especificaciones t\u00e9cnicas de Rockchip<\/a>, esta NPU consta de tres n\u00facleos independientes, lo que permite una asignaci\u00f3n flexible de tareas o el procesamiento en paralelo de m\u00faltiples pipelines de modelos. Esta arquitectura garantiza que <b data-path-to-node=\"8\" data-index-in-node=\"505\">RK3588 6 TOPS borde AI<\/b> se mantienen estables incluso en condiciones de ralentizaci\u00f3n t\u00e9rmica, a diferencia de los chips para m\u00f3viles.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-95a0e1e elementor-widget elementor-widget-text-editor\" data-id=\"95a0e1e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"9\">Pruebas de rendimiento en el mundo real: RK3588 6 TOPS Edge AI en acci\u00f3n<\/h2><p data-path-to-node=\"10\"><span style=\"font-size: 14pt;\">Para comprender lo que <b data-path-to-node=\"12\" data-index-in-node=\"19\">RK3588 6 TOPS borde AI<\/b> entrega, debemos fijarnos en los puntos de referencia estandarizados. Aunque la generaci\u00f3n anterior era un punto de partida s\u00f3lido, nuestra comparaci\u00f3n detallada de <b data-path-to-node=\"12\" data-index-in-node=\"171\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/es\/rk3588-vs-rk3399-edge-ai-performance\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwjqroT_8bWUAxUAAAAAHQAAAAAQag\">Rendimiento de la IA en los bordes del RK3588 frente al RK3399<\/a><\/b> muestra un salto de 10 veces en la velocidad de inferencia gracias a la NPU dedicada.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ebd3f8 elementor-widget elementor-widget-text-editor\" data-id=\"6ebd3f8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 data-path-to-node=\"11\">Tabla de datos de rendimiento (cuantizaci\u00f3n INT8)<\/h3><table data-path-to-node=\"12\"><thead><tr><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>Modelo<\/strong><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>Marco<\/strong><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>Latencia (ms)<\/strong><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>FPS<\/strong><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>Caso pr\u00e1ctico<\/strong><\/span><\/td><\/tr><\/thead><tbody><tr><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,1,0,0\"><b data-path-to-node=\"12,1,0,0\" data-index-in-node=\"0\">ResNet18<\/b><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,1,1,0\">PyTorch<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,1,2,0\">4.09<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,1,3,0\">244<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,1,4,0\">Inspecci\u00f3n de calidad<\/span><\/td><\/tr><tr><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,2,0,0\"><b data-path-to-node=\"12,2,0,0\" data-index-in-node=\"0\">YOLOv5s<\/b><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,2,1,0\">ONNX<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,2,2,0\">18.5<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,2,3,0\">54<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,2,4,0\">Detecci\u00f3n de objetos<\/span><\/td><\/tr><tr><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,3,0,0\"><b data-path-to-node=\"12,3,0,0\" data-index-in-node=\"0\">YOLOv8n<\/b><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,3,1,0\">PyTorch<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,3,2,0\">15.2<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,3,3,0\">65<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,3,4,0\">Seguimiento en tiempo real<\/span><\/td><\/tr><tr><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,4,0,0\"><b data-path-to-node=\"12,4,0,0\" data-index-in-node=\"0\">MobileNetV2<\/b><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,4,1,0\">TF Lite<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,4,2,0\">5.0<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,4,3,0\">200<\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\" data-path-to-node=\"12,4,4,0\">Reconocimiento de gestos<\/span><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8c87c4e elementor-widget elementor-widget-text-editor\" data-id=\"8c87c4e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"13\"><span style=\"font-size: 14pt;\"><i data-path-to-node=\"13\" data-index-in-node=\"0\">Fuente de datos: Recopilados a partir de<a href=\"https:\/\/www.cnx-software.com\/\" target=\"_blank\" rel=\"noopener\"> Software CNX<\/a> y pruebas de laboratorio internas ieeker.<\/i><\/span><\/p><p data-path-to-node=\"14\"><span style=\"font-size: 14pt;\">Aunque la NPU destaca en arquitecturas basadas en CNN, los desarrolladores que exploran <b data-path-to-node=\"14\" data-index-in-node=\"70\">Referencia de inferencia RKNN-Toolkit2<\/b> resultados observar\u00e1n que los modelos basados en transformadores (como ViT) pueden requerir una mayor optimizaci\u00f3n. Sin embargo, para 90% de ciclos industriales de \"detectar y actuar\", la latencia de 15-20 ms proporcionada por RK3588 est\u00e1 dentro de los requisitos para la automatizaci\u00f3n a velocidad de l\u00ednea.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1f0e703 elementor-widget elementor-widget-image\" data-id=\"1f0e703\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"386\" src=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RK3588-AI-Inference-FPS-Comparison-1-1024x494.webp\" class=\"attachment-large size-large wp-image-10255\" alt=\"Gr\u00e1fico de barras que muestra las pruebas comparativas de FPS de RK3588 para los modelos YOLOv5 y YOLOv8 utilizando la cuantizaci\u00f3n INT8.\" srcset=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RK3588-AI-Inference-FPS-Comparison-1-1024x494.webp 1024w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RK3588-AI-Inference-FPS-Comparison-1-300x145.webp 300w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RK3588-AI-Inference-FPS-Comparison-1-768x371.webp 768w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RK3588-AI-Inference-FPS-Comparison-1-18x9.webp 18w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RK3588-AI-Inference-FPS-Comparison-1.webp 1376w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a014d57 elementor-widget elementor-widget-text-editor\" data-id=\"a014d57\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"15\">Dominio del flujo de trabajo de la prueba comparativa de inferencia RKNN-Toolkit2<\/h2><p data-path-to-node=\"16\"><span style=\"font-size: 14pt;\">Alcanzar la cima <b data-path-to-node=\"16\" data-index-in-node=\"15\">Referencia de inferencia RKNN-Toolkit2<\/b> resultados requiere un proceso de despliegue disciplinado. El conjunto de herramientas act\u00faa como puente entre marcos populares como PyTorch o TensorFlow y el hardware Rockchip. La etapa m\u00e1s cr\u00edtica es <b data-path-to-node=\"16\" data-index-in-node=\"240\">An\u00e1lisis de cuantizaci\u00f3n<\/b>.<\/span><\/p><ol start=\"1\" data-path-to-node=\"17\"><li><p data-path-to-node=\"17,0,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"17,0,0\" data-index-in-node=\"0\">Exportar:<\/b> Convierta su modelo entrenado a un formato neutro (normalmente ONNX).<\/span><\/p><\/li><li><p data-path-to-node=\"17,1,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"17,1,0\" data-index-in-node=\"0\">Conversi\u00f3n:<\/b> Utilice RKNN-Toolkit2 para transformar el archivo ONNX en un archivo <code data-path-to-node=\"17,1,0\" data-index-in-node=\"68\">.rknn<\/code> binario.<\/span><\/p><\/li><li><p data-path-to-node=\"17,2,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"17,2,0\" data-index-in-node=\"0\">Cuantizaci\u00f3n:<\/b> Proporcionar un \"conjunto de datos de calibraci\u00f3n\" (normalmente 100-200 im\u00e1genes representativas).<\/span><\/p><\/li><li><p data-path-to-node=\"17,3,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"17,3,0\" data-index-in-node=\"0\">Despliegue:<\/b> Utilizar la API RKNN Runtime C++ o Python en la placa.<\/span><\/p><\/li><\/ol><p data-path-to-node=\"18\"><span style=\"font-size: 14pt;\">Un error com\u00fan es ignorar el \"Soporte de Operador\". Si su modelo utiliza una funci\u00f3n de activaci\u00f3n personalizada no soportada por la NPU, el kit de herramientas descargar\u00e1 esa capa a la CPU. Esta \"CPU Fallback\" puede aumentar la latencia en 500% o m\u00e1s. Verifique siempre sus capas con la \u00faltima <a class=\"ng-star-inserted\" href=\"https:\/\/optee.readthedocs.io\/en\/latest\/general\/platforms.html\" target=\"_blank\" rel=\"noopener\" data-hveid=\"37\">Lista de Op-Support de Rockchip<\/a>.<\/span><\/p><h3 data-path-to-node=\"19,0\">\ud83d\udca1 Consigue una evaluaci\u00f3n profesional de la viabilidad de la IA<\/h3><p data-path-to-node=\"19,1\">No adivine el rendimiento de su proyecto bas\u00e1ndose \u00fanicamente en las especificaciones en bruto. Nuestro equipo de ingenieros le <b data-path-to-node=\"19,1\" data-index-in-node=\"97\">servicio gratuito de preevaluaci\u00f3n de modelos<\/b>. <b data-path-to-node=\"19,1\" data-index-in-node=\"132\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/es\/contact-us\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQhQE\">Obtenga un presupuesto personalizado y asistencia t\u00e9cnica \u2192<\/a><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bf7f02e elementor-widget elementor-widget-image\" data-id=\"bf7f02e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"437\" data-src=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RKNN-Toolkit2-Workflow-Diagram-1024x559.webp\" class=\"attachment-large size-large wp-image-10251 lazyload\" alt=\"\" data-srcset=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RKNN-Toolkit2-Workflow-Diagram-1024x559.webp 1024w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RKNN-Toolkit2-Workflow-Diagram-300x164.webp 300w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RKNN-Toolkit2-Workflow-Diagram-768x419.webp 768w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RKNN-Toolkit2-Workflow-Diagram-18x10.webp 18w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/RKNN-Toolkit2-Workflow-Diagram.webp 1408w\" data-sizes=\"(max-width: 800px) 100vw, 800px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/437;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f89554 elementor-widget elementor-widget-text-editor\" data-id=\"3f89554\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"19\">Desde la f\u00e1brica: Un reto de cuantificaci\u00f3n en el mundo real<\/h2><p data-path-to-node=\"20\"><span style=\"font-size: 14pt;\">En una implantaci\u00f3n reciente para un fabricante de PCBA, nos encontramos con el cl\u00e1sico muro de \"precisi\u00f3n frente a velocidad\". El cliente utilizaba un <b data-path-to-node=\"21\" data-index-in-node=\"124\">ResNet50<\/b> para la detecci\u00f3n de defectos de soldadura a alta velocidad. Como especialista en <b data-path-to-node=\"21\" data-index-in-node=\"204\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/es\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQhgE\">Fabricaci\u00f3n de placas de desarrollo<\/a><\/b>Entendemos que la estabilidad del hardware es tan importante como la optimizaci\u00f3n del software.<\/span><\/p><p data-path-to-node=\"21\"><span style=\"font-size: 14pt;\">Cuando pasamos a la NPU RK3588 utilizando la cuantizaci\u00f3n INT8 est\u00e1ndar, la precisi\u00f3n cay\u00f3 en picado hasta 73%. \u00bfEl culpable? El conjunto de datos de calibraci\u00f3n utilizado durante la conversi\u00f3n RKNN era demasiado peque\u00f1o y carec\u00eda de ejemplos \"negativos\" (placas limpias).<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ab2e58 elementor-widget elementor-widget-text-editor\" data-id=\"6ab2e58\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-size: 14pt;\"><b data-path-to-node=\"23\" data-index-in-node=\"0\">La soluci\u00f3n:<\/b> Ampliamos el conjunto de calibraci\u00f3n a 500 im\u00e1genes y utilizamos <b data-path-to-node=\"23\" data-index-in-node=\"73\">H\u00edbrido <\/b><\/span><\/p><p><span style=\"font-size: 14pt;\"><b data-path-to-node=\"23\" data-index-in-node=\"73\">Cuantizaci\u00f3n<\/b>-manteniendo las capas finales totalmente conectadas en FP16 mientras que las capas convolucionales pesadas se ejecutaban en INT8. <\/span><\/p><p><span style=\"font-size: 14pt;\"><b data-path-to-node=\"23\" data-index-in-node=\"192\">El resultado:<\/b> La precisi\u00f3n volvi\u00f3 a 88,9%, con una latencia de 28ms por placa, satisfaciendo los requisitos de velocidad de l\u00ednea.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae9e092 elementor-widget elementor-widget-image\" data-id=\"ae9e092\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"534\" data-src=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-Deployment-1024x683.webp\" class=\"attachment-large size-large wp-image-10250 lazyload\" alt=\"RK3588 vs Jetson\" data-srcset=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-Deployment-1024x683.webp 1024w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-Deployment-300x200.webp 300w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-Deployment-768x512.webp 768w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-Deployment-18x12.webp 18w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-Deployment.webp 1536w\" data-sizes=\"(max-width: 800px) 100vw, 800px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/534;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-18840fa elementor-widget elementor-widget-text-editor\" data-id=\"18840fa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"23\">Caso pr\u00e1ctico del proyecto: Nodo de tr\u00e1fico inteligente con 16 c\u00e1maras<\/h2><p data-path-to-node=\"24\"><span style=\"font-size: 14pt;\">Recientemente hemos implantado un nodo de gesti\u00f3n del tr\u00e1fico urbano aprovechando nuestra <b data-path-to-node=\"5,1,0\" data-index-in-node=\"23\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/es\/products\/yky-3588s-rk3588s-8k-ai-sbc\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwjqroT_8bWUAxUAAAAAHQAAAAAQVw\">Ordenador industrial RK3588<\/a><\/b>preintegrado con interfaces Gigabit Ethernet y PCIe duales para implantaciones perif\u00e9ricas robustas. El requisito era procesar 16 secuencias RTSP 720P simult\u00e1neas para detectar matr\u00edculas y tipos de veh\u00edculos.<\/span><\/p><p data-path-to-node=\"25\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"25\" data-index-in-node=\"0\">Configuraci\u00f3n del hardware:<\/b><\/span><\/p><ul data-path-to-node=\"26\"><li><p data-path-to-node=\"26,0,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"26,0,0\" data-index-in-node=\"0\">N\u00facleo:<\/b> RK3588 (4x A76 + 4x A55)<\/span><\/p><\/li><li><p data-path-to-node=\"26,1,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"26,1,0\" data-index-in-node=\"0\">NPU:<\/b> 6 TOPS (cl\u00faster de 3 n\u00facleos)<\/span><\/p><\/li><li><p data-path-to-node=\"26,2,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"26,2,0\" data-index-in-node=\"0\">Refrigeraci\u00f3n:<\/b> Chasis de aluminio sin ventilador (pasivo)<\/span><\/p><\/li><\/ul><p data-path-to-node=\"27\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"27\" data-index-in-node=\"0\">Estrategia de despliegue:<\/b> Aprovechando la NPU multin\u00facleo, asignamos 5-6 flujos por n\u00facleo de NPU. Utilizamos un <b data-path-to-node=\"27\" data-index-in-node=\"110\">MobileNet-SSD<\/b> arquitectura adaptada a la localizaci\u00f3n de matr\u00edculas.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a46d8cc elementor-widget elementor-widget-image\" data-id=\"a46d8cc\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"534\" data-src=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Industrial-Inspection-1024x683.webp\" class=\"attachment-large size-large wp-image-10253 lazyload\" alt=\"Inspecci\u00f3n industrial\" data-srcset=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Industrial-Inspection-1024x683.webp 1024w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Industrial-Inspection-300x200.webp 300w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Industrial-Inspection-768x512.webp 768w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Industrial-Inspection-18x12.webp 18w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Industrial-Inspection.webp 1536w\" data-sizes=\"(max-width: 800px) 100vw, 800px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/534;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e66d31d elementor-widget elementor-widget-text-editor\" data-id=\"e66d31d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"27\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"27\" data-index-in-node=\"178\">Los datos:<\/b><\/span><\/p><ul data-path-to-node=\"28\"><li><p data-path-to-node=\"28,0,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"28,0,0\" data-index-in-node=\"0\">Rendimiento:<\/b> 18-20 FPS por canal en los 16 canales.<\/span><\/p><\/li><li><p data-path-to-node=\"28,1,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"28,1,0\" data-index-in-node=\"0\">Consumo de energ\u00eda:<\/b> Todo el sistema consume s\u00f3lo 7,4 W a plena carga de IA.<\/span><\/p><\/li><li><p data-path-to-node=\"28,2,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"28,2,0\" data-index-in-node=\"0\">Comparaci\u00f3n:<\/b> Una similar <b data-path-to-node=\"28,2,0\" data-index-in-node=\"22\">caso conocido internacionalmente<\/b> El uso de NVIDIA Jetson Orin NX (25 W) proporcion\u00f3 m\u00e1s FPS, pero con un coste de hardware tres veces superior y una emisi\u00f3n de calor significativamente mayor, lo que habr\u00eda requerido refrigeraci\u00f3n activa, un punto de fallo en los polvorientos armarios de carretera.<\/span><\/p><\/li><\/ul><p data-path-to-node=\"29\"><span style=\"font-size: 14pt;\">Este proyecto demostr\u00f3 que, para los nodos de borde distribuidos, los 6 TOPS del RK3588 son el \"punto dulce\" para equilibrar la capacidad multiflujo y la fiabilidad t\u00e9rmica.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2dae31d elementor-widget elementor-widget-image\" data-id=\"2dae31d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"534\" data-src=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-1024x683.webp\" class=\"attachment-large size-large wp-image-10252 lazyload\" alt=\"\" data-srcset=\"https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-1024x683.webp 1024w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-300x200.webp 300w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-768x512.webp 768w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial-18x12.webp 18w, https:\/\/ieeker.com\/wp-content\/uploads\/2026\/05\/Real-world-RK3588-Industrial.webp 1536w\" data-sizes=\"(max-width: 800px) 100vw, 800px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/534;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd6a7b0 elementor-widget elementor-widget-text-editor\" data-id=\"cd6a7b0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"30\">\u00bfSon suficientes 6 TOPS? Matriz de decisi\u00f3n final<\/h2><p data-path-to-node=\"31\"><span style=\"font-size: 14pt;\">Antes de elegir su hardware, h\u00e1gase estas tres preguntas:<\/span><\/p><ol start=\"1\" data-path-to-node=\"32\"><li><p data-path-to-node=\"32,0,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"32,0,0\" data-index-in-node=\"0\">\u00bfSu modelo est\u00e1 basado en CNN?<\/b> En caso afirmativo (YOLO, ResNet, SSD), RK3588 es excelente.<\/span><\/p><\/li><li><p data-path-to-node=\"32,1,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"32,1,0\" data-index-in-node=\"0\">\u00bfCu\u00e1l es su presupuesto de latencia?<\/b> Si necesita &lt;10ms para un modelo YOLOv8l complejo, puede que necesite un acelerador de m\u00e1s de 20 TOPS como el <a class=\"ng-star-inserted\" href=\"https:\/\/hailo.ai\/de\/products\/ai-accelerators\/hailo-8-m2-ai-acceleration-module\/#hailo8-m2-uberblick\" target=\"_blank\" rel=\"noopener\" data-hveid=\"38\">Hailo-8<\/a>.<\/span><\/p><\/li><li><p data-path-to-node=\"32,2,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"32,2,0\" data-index-in-node=\"0\">\u00bfNecesita su sistema algo m\u00e1s que inteligencia artificial?<\/b> Si adem\u00e1s necesitas codificar v\u00eddeo 4K o ejecutar un servidor web, la CPU octa-core y la VPU 8K del RK3588 lo hacen superior a los chips \"s\u00f3lo IA\".<\/span><\/p><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d8b112 elementor-widget elementor-widget-text-editor\" data-id=\"5d8b112\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-size: 14pt;\">Si no est\u00e1 seguro de si 6 TOPS son suficientes para su modelo, puede <b data-path-to-node=\"30\" data-index-in-node=\"62\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/es\/contact-us\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQiAE\">solicite presupuesto y asesoramiento t\u00e9cnico<\/a><\/b> de nuestro equipo de ingenieros.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-07d04ba elementor-widget elementor-widget-text-editor\" data-id=\"07d04ba\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"3\">Conclusiones: C\u00f3mo aprovechar al m\u00e1ximo el rendimiento de la NPU RK3588<\/h2><p data-path-to-node=\"4\"><span style=\"font-size: 14pt;\">En <b data-path-to-node=\"4\" data-index-in-node=\"4\">Rendimiento de la NPU RK3588<\/b> es un revulsivo para la computaci\u00f3n de borde industrial, pero s\u00f3lo si se combina con el enfoque de ingenier\u00eda adecuado. Aunque \"6 TOPS\" es la cifra m\u00e1s destacada, el valor real reside en la capacidad del chip para funcionar a alta velocidad. <b data-path-to-node=\"4\" data-index-in-node=\"236\">INT8<\/b> mientras gestiona simult\u00e1neamente flujos de v\u00eddeo 8K y complejas tareas de E\/S.<\/span><\/p><p data-path-to-node=\"5\"><span style=\"font-size: 14pt;\">Para los ingenieros, el camino hacia el \u00e9xito pasa por una s\u00f3lida <b data-path-to-node=\"5\" data-index-in-node=\"53\">Referencia de inferencia RKNN-Toolkit2<\/b> centrada en la cuantificaci\u00f3n de alta calidad y la optimizaci\u00f3n del operador. Para los jefes de proyecto, ofrece una forma de conseguir resultados \"de nivel Jetson\" a un precio mucho m\u00e1s competitivo y con un presupuesto de energ\u00eda m\u00e1s ajustado. Tanto si est\u00e1 construyendo un sistema AOI para una f\u00e1brica como un monitor de tr\u00e1fico de m\u00faltiples flujos, el RK3588 proporciona el margen necesario para la pr\u00f3xima generaci\u00f3n de sistemas de cuantificaci\u00f3n y cuantificaci\u00f3n. <b data-path-to-node=\"5\" data-index-in-node=\"464\">despliegues edge AI<\/b>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-98db0dd elementor-widget elementor-widget-heading\" data-id=\"98db0dd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">PREGUNTAS FRECUENTES<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9783c08 elementor-widget elementor-widget-n-accordion\" data-id=\"9783c08\" data-element_type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;expanded&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Acorde\u00f3n. Abra los enlaces con Intro o Espacio, ci\u00e9rrelos con Escape y navegue con las teclas de flecha.\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1580\" class=\"e-n-accordion-item\" open>\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"true\" aria-controls=\"e-n-accordion-item-1580\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> P: \u00bfPuedo ejecutar Llama-3 en RK3588? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1580\" class=\"elementor-element elementor-element-3b3cba1 e-con-full e-flex e-con e-child\" data-id=\"3b3cba1\" data-element_type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-125f6c1 elementor-widget elementor-widget-text-editor\" data-id=\"125f6c1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-size: 14pt;\">R: Los modelos ling\u00fc\u00edsticos de gran tama\u00f1o (8B+) superan la capacidad de gesti\u00f3n de memoria de la NPU. Sin embargo, los modelos con par\u00e1metros de 1B-2B como TinyLlama o Qwen-1.8B se ejecutan eficazmente a ~15 tokens\/s utilizando la NPU. <a class=\"ng-star-inserted\" href=\"https:\/\/github.com\/airockchip\/rknn-llm\" target=\"_blank\" rel=\"noopener\" data-hveid=\"48\">Tiempo de ejecuci\u00f3n de RKLLM<\/a>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1581\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1581\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> P: \u00bfEs compatible el RK3588 con TensorFlow? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1581\" class=\"elementor-element elementor-element-127feee e-con-full e-flex e-con e-child\" data-id=\"127feee\" data-element_type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5a7cdf4 elementor-widget elementor-widget-text-editor\" data-id=\"5a7cdf4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-size: 14pt;\">R: S\u00ed, a trav\u00e9s de la conversi\u00f3n a ONNX o directamente a trav\u00e9s del frontend RKNN-Toolkit2 TensorFlow.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1582\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1582\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> P: \u00bfC\u00f3mo gestiona la NPU el calor en entornos industriales? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1582\" class=\"elementor-element elementor-element-04d5e19 e-con-full e-flex e-con e-child\" data-id=\"04d5e19\" data-element_type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f566bca elementor-widget elementor-widget-text-editor\" data-id=\"f566bca\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-size: 14pt;\">R: Con una carga completa de 6 TOPS, la NPU a\u00f1ade aproximadamente 2-3W al consumo de energ\u00eda del SoC. En un <b data-path-to-node=\"10\" data-index-in-node=\"148\">ieeker<\/b> chasis industrial sin ventilador, puede mantener el m\u00e1ximo rendimiento a temperaturas ambiente de hasta 60 \u00b0C.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1583\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1583\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> P: \u00bfD\u00f3nde puedo obtener asistencia t\u00e9cnica para las placas ieeker? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1583\" class=\"elementor-element elementor-element-5bd91a2 e-con-full e-flex e-con e-child\" data-id=\"5bd91a2\" data-element_type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-17b08a1 elementor-widget elementor-widget-text-editor\" data-id=\"17b08a1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-size: 14pt;\">R: Visite nuestro <b data-path-to-node=\"34\" data-index-in-node=\"69\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/es\/support\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQiQE\">soporte t\u00e9cnico para placas ieeker<\/a><\/b> para consultar la documentaci\u00f3n y los scripts RKNN.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d5ba9d elementor-widget elementor-widget-text-editor\" data-id=\"8d5ba9d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-path-to-node=\"12\">Referencias<\/h2><ul data-path-to-node=\"13\"><li><p data-path-to-node=\"13,0,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"13,0,0\" data-index-in-node=\"0\">Definiciones t\u00e9cnicas:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"49\">TOPS (operaciones de tera por segundo) - Wikipedia<\/a><\/span><\/p><\/li><li><p data-path-to-node=\"13,1,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"13,1,0\" data-index-in-node=\"0\">Cadena de herramientas oficial:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"50\">Repositorio GitHub de Rockchip RKNN-Toolkit2<\/a><\/span><\/p><\/li><li><p data-path-to-node=\"13,2,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"13,2,0\" data-index-in-node=\"0\">Normas del sector:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"51\">Embedded Vision Alliance - Evaluaci\u00f3n comparativa de procesadores<\/a><\/span><\/p><\/li><li><p data-path-to-node=\"13,3,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"13,3,0\" data-index-in-node=\"0\">Par\u00e1metros de rendimiento:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"52\">CNX Software - Rockchip RK3588 AI Pruebas de rendimiento<\/a><\/span><\/p><\/li><li><p data-path-to-node=\"13,4,0\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"13,4,0\" data-index-in-node=\"0\">Datos comparativos:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"53\">Especificaciones t\u00e9cnicas de NVIDIA Jetson Orin Nano<\/a><\/span><\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>El rendimiento de la NPU RK3588 determina si su dispositivo de borde puede manejar la inferencia de IA en tiempo real sin una GPU dedicada. Para la mayor\u00eda de las tareas de visi\u00f3n industrial, su NPU de 6 TOPS ofrece m\u00e1s de 50 FPS para YOLOv5s y sustituye eficazmente a los m\u00f3dulos Jetson de NVIDIA de gama b\u00e1sica al equilibrar el coste, la potencia y la integraci\u00f3n de E\/S. Puntos clave Benchmark de rendimiento: RK3588 alcanza m\u00e1s de 54 FPS en [...]<\/p>","protected":false},"author":2,"featured_media":10249,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-10246","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>RK3588 NPU Performance: What 6 TOPS Means for Industrial AI | IEEKER<\/title>\n<meta name=\"description\" content=\"Discover the real RK3588 NPU performance with YOLOv5 benchmarks (54+ FPS) and RKNN-Toolkit2 quantization tips. 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