{"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\/de\/rk3588-npu-performance-industrial-edge-ai\/","title":{"rendered":"RK3588 NPU-Leistung: Was 6 TOPS wirklich f\u00fcr Ihr industrielles KI-Projekt bedeuten"},"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\">RK3588 NPU-Leistung<\/b> bestimmt, ob Ihr Edge-Ger\u00e4t KI-Inferenzen in Echtzeit ohne einen dedizierten Grafikprozessor verarbeiten kann. F\u00fcr die meisten industriellen Bildverarbeitungsaufgaben liefert die 6 TOPS NPU \u00fcber 50 FPS f\u00fcr YOLOv5s und ersetzt effektiv NVIDIA Jetson-Module der Einstiegsklasse, indem sie Kosten, Leistung und E\/A-Integration ausgleicht.<\/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\">Wichtigste Erkenntnisse<\/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\">Leistungs-Benchmark:<\/b> RK3588 erreicht 54+ FPS auf YOLOv5s (INT8) und 244 FPS auf 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\">Auf die Pr\u00e4zision kommt es an:<\/b> 6 TOPS entsprechen der INT8-Spitzenleistung; die FP16-Leistung ist deutlich geringer (~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\">Arbeitsablauf:<\/b> Erfordert <a class=\"ng-star-inserted\" href=\"https:\/\/github.com\/rockchip-linux\/rknn-toolkit2\" target=\"_blank\" rel=\"noopener\" data-hveid=\"4\">RKNN-Toolkit2<\/a> f\u00fcr die Modellumwandlung und Quantisierung.<\/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\">Kosteneffizienz:<\/b> Bietet einen konsolidierten SoC-Ansatz (CPU\/GPU\/NPU\/ISP), der die Gesamtkosten im Vergleich zu diskreten KI-Beschleunigern reduziert.<\/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\">Industrielle Passform:<\/b> Ideal f\u00fcr Leistungsbereiche unter 10 W, in denen die Verarbeitung mehrerer Kameras erforderlich ist.<\/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\">Was bedeutet eigentlich \"RK3588 NPU-Leistung\"?<\/h2><p data-path-to-node=\"7\"><span style=\"font-size: 14pt;\">Bei der Evaluierung <b data-path-to-node=\"7\" data-index-in-node=\"16\">RK3588 NPU-Leistung<\/b>ist es von entscheidender Bedeutung, zwischen den \"Spitzen\"-Zahlen des Marketings und der Realit\u00e4t des Einsatzes zu unterscheiden. In der Welt des 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-Operationen pro Sekunde)<\/a> ist die Standard-Metrik. Die 6 TOPS des RK3588 sind jedoch speziell f\u00fcr INT8-Operationen (8-Bit-Integer) optimiert, die bei Deep-Learning-Inferenzen \u00fcblich sind.<\/span><\/p><p data-path-to-node=\"8\"><span style=\"font-size: 14pt;\">Wenn Ihr Projekt hochpr\u00e4zise Flie\u00dfkommamathematik (FP32) erfordert, ist die NPU nicht das richtige Werkzeug - Sie w\u00fcrden auf die CPU oder GPU zur\u00fcckgreifen, wo die Leistung drastisch sinkt. F\u00fcr industrielle KI ist das Ziel fast immer <b data-path-to-node=\"8\" data-index-in-node=\"219\">Quantisierung<\/b>: Umstellung der Modelle auf INT8, um die vollen 6 TOPS zu nutzen. Gem\u00e4\u00df <a class=\"ng-star-inserted\" href=\"https:\/\/en.wikipedia.org\/wiki\/List_of_Rockchip_products\" target=\"_blank\" rel=\"noopener\" data-hveid=\"6\">Die technischen Daten von Rockchip<\/a>Diese NPU besteht aus drei unabh\u00e4ngigen Kernen, die eine flexible Aufgabenzuweisung oder die parallele Verarbeitung mehrerer Modellpipelines erm\u00f6glichen. Diese Architektur gew\u00e4hrleistet, dass <b data-path-to-node=\"8\" data-index-in-node=\"505\">RK3588 6 TOPS Kante AI<\/b> F\u00e4higkeiten bleiben auch bei thermischer Drosselung stabil, anders als bei Chips f\u00fcr den mobilen Einsatz.<\/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\">Real-World Benchmarks: RK3588 6 TOPS Edge AI in Aktion<\/h2><p data-path-to-node=\"10\"><span style=\"font-size: 14pt;\">Um zu verstehen, was <b data-path-to-node=\"12\" data-index-in-node=\"19\">RK3588 6 TOPS Kante AI<\/b> liefert, m\u00fcssen wir auf standardisierte Benchmarks zur\u00fcckgreifen. Die \u00e4ltere Generation war zwar ein solider Einstieg, aber unser detaillierter Vergleich von <b data-path-to-node=\"12\" data-index-in-node=\"171\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/de\/rk3588-vs-rk3399-edge-ai-performance\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwjqroT_8bWUAxUAAAAAHQAAAAAQag\">RK3588 vs. RK3399 Kanten-KI-Leistung<\/a><\/b> zeigt einen 10-fachen Sprung in der Inferenzgeschwindigkeit dank der dedizierten NPU.<\/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\">Leistungsdatentabelle (INT8-Quantisierung)<\/h3><table data-path-to-node=\"12\"><thead><tr><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>Modell<\/strong><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>Rahmenwerk<\/strong><\/span><\/td><td style=\"text-align: center;\"><span style=\"font-size: 14pt;\"><strong>Latenzzeit (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>Anwendungsfall<\/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\">Qualit\u00e4tskontrolle<\/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\">Objekt-Erkennung<\/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\">Verfolgung in Echtzeit<\/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\">Erkennung von Gesten<\/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\">Datenquelle: Zusammengestellt aus<a href=\"https:\/\/www.cnx-software.com\/\" target=\"_blank\" rel=\"noopener\"> CNX-Software<\/a> und ieeker-interne Labortests.<\/i><\/span><\/p><p data-path-to-node=\"14\"><span style=\"font-size: 14pt;\">W\u00e4hrend sich die NPU bei CNN-basierten Architekturen auszeichnet, erforschen Entwickler <b data-path-to-node=\"14\" data-index-in-node=\"70\">RKNN-Toolkit2 Inferenz-Benchmark<\/b> Die Ergebnisse zeigen, dass transformatorbasierte Modelle (wie ViT) m\u00f6glicherweise mehr Optimierung erfordern. F\u00fcr 90% der industriellen \"Detect-and-Act\"-Zyklen ist die von RK3588 bereitgestellte Latenz von 15-20 ms jedoch durchaus im Rahmen der Anforderungen f\u00fcr die Automatisierung bei Liniengeschwindigkeit.<\/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=\"Balkendiagramm mit RK3588 FPS-Benchmarks f\u00fcr YOLOv5- und YOLOv8-Modelle mit INT8-Quantisierung.\" 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\">Beherrschung des RKNN-Toolkit2 Inferenz-Benchmark-Workflows<\/h2><p data-path-to-node=\"16\"><span style=\"font-size: 14pt;\">Erreichen von Spitzenwerten <b data-path-to-node=\"16\" data-index-in-node=\"15\">RKNN-Toolkit2 Inferenz-Benchmark<\/b> Ergebnisse erfordert eine disziplinierte Einsatzpipeline. Das Toolkit fungiert als Br\u00fccke zwischen g\u00e4ngigen Frameworks wie PyTorch oder TensorFlow und der Rockchip-Hardware. Die kritischste Phase ist <b data-path-to-node=\"16\" data-index-in-node=\"240\">Quantisierungsanalyse<\/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\">Ausfuhr:<\/b> Konvertieren Sie Ihr trainiertes Modell in ein neutrales Format (normalerweise 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\">Umwandlung:<\/b> Verwenden Sie das RKNN-Toolkit2 zur Umwandlung der ONNX-Datei in eine <code data-path-to-node=\"17,1,0\" data-index-in-node=\"68\">.rknn<\/code> bin\u00e4r.<\/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\">Quantisierung:<\/b> Stellen Sie einen \"Kalibrierungsdatensatz\" bereit (in der Regel 100-200 repr\u00e4sentative Bilder).<\/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\">Einsatz:<\/b> Verwenden Sie die RKNN Runtime C++ oder Python API auf dem Board.<\/span><\/p><\/li><\/ol><p data-path-to-node=\"18\"><span style=\"font-size: 14pt;\">Ein h\u00e4ufiger Fallstrick ist das Ignorieren der \"Operator-Unterst\u00fctzung\". Wenn Ihr Modell eine benutzerdefinierte Aktivierungsfunktion verwendet, die von der NPU nicht unterst\u00fctzt wird, \u00fcbertr\u00e4gt das Toolkit diese Schicht an die CPU. Dieses \"CPU Fallback\" kann die Latenzzeit um 500% oder mehr erh\u00f6hen. \u00dcberpr\u00fcfen Sie Ihre Schichten immer anhand der neuesten <a class=\"ng-star-inserted\" href=\"https:\/\/optee.readthedocs.io\/en\/latest\/general\/platforms.html\" target=\"_blank\" rel=\"noopener\" data-hveid=\"37\">Rockchip Op-Support Liste<\/a>.<\/span><\/p><h3 data-path-to-node=\"19,0\">\ud83d\udca1 Erhalten Sie eine professionelle KI-Durchf\u00fchrbarkeitsbewertung<\/h3><p data-path-to-node=\"19,1\">Sch\u00e4tzen Sie die Leistung Ihres Projekts nicht nur auf der Grundlage von Rohdaten. Unser Ingenieurteam bietet eine <b data-path-to-node=\"19,1\" data-index-in-node=\"97\">kostenloser Service zur Vorabbeurteilung von Modellen<\/b>. <b data-path-to-node=\"19,1\" data-index-in-node=\"132\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/de\/contact-us\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQhQE\">Individuelles Angebot &amp; technischer Support \u2192 anfordern<\/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\">Aus der Fabrikhalle: Eine Quantisierungsherausforderung in der realen Welt<\/h2><p data-path-to-node=\"20\"><span style=\"font-size: 14pt;\">Bei einem k\u00fcrzlichen Einsatz f\u00fcr einen PCBA-Hersteller stie\u00dfen wir auf eine klassische \"Pr\u00e4zision vs. Geschwindigkeit\"-Wand. Der Kunde verwendete eine <b data-path-to-node=\"21\" data-index-in-node=\"124\">ResNet50<\/b> Backbone f\u00fcr die Hochgeschwindigkeits-L\u00f6tfehlererkennung. Als Spezialist f\u00fcr <b data-path-to-node=\"21\" data-index-in-node=\"204\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/de\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQhgE\">Herstellung von Entwicklungsplatinen<\/a><\/b>Wir wissen, dass die Stabilit\u00e4t der Hardware ebenso wichtig ist wie die Optimierung der Software.<\/span><\/p><p data-path-to-node=\"21\"><span style=\"font-size: 14pt;\">Als wir zur RK3588 NPU mit Standard-INT8-Quantisierung wechselten, sank die Genauigkeit auf 73%. Der Schuldige? Der bei der RKNN-Konvertierung verwendete Kalibrierungsdatensatz war zu klein und enthielt keine \"negativen\" Beispiele (saubere Platinen).<\/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\">Die L\u00f6sung:<\/b> Wir erweiterten den Kalibrierungssatz auf 500 Bilder und nutzten die <b data-path-to-node=\"23\" data-index-in-node=\"73\">Hybride <\/b><\/span><\/p><p><span style=\"font-size: 14pt;\"><b data-path-to-node=\"23\" data-index-in-node=\"73\">Quantisierung<\/b>-Schichten in FP16 zu halten, w\u00e4hrend die schweren Faltungsschichten in INT8 laufen. <\/span><\/p><p><span style=\"font-size: 14pt;\"><b data-path-to-node=\"23\" data-index-in-node=\"192\">Das Ergebnis:<\/b> Die Genauigkeit stieg auf 88,9% mit einer Latenzzeit von 28 ms pro Karte, was die Anforderungen an die Leitungsgeschwindigkeit erf\u00fcllt.<\/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 gegen 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\">Projekt Fallstudie: Intelligenter Verkehrsknotenpunkt mit 16 Kameras<\/h2><p data-path-to-node=\"24\"><span style=\"font-size: 14pt;\">Wir haben vor kurzem einen st\u00e4dtischen Verkehrsmanagementknotenpunkt eingerichtet, der unsere <b data-path-to-node=\"5,1,0\" data-index-in-node=\"23\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/de\/products\/yky-3588s-rk3588s-8k-ai-sbc\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwjqroT_8bWUAxUAAAAAHQAAAAAQVw\">Industrierechner RK3588<\/a><\/b>die mit zwei Gigabit-Ethernet- und PCIe-Schnittstellen f\u00fcr robuste Edge-Implementierungen vorintegriert ist. Die Anforderung bestand darin, 16 gleichzeitige 720P-RTSP-Streams zu verarbeiten, um Nummernschilder und Fahrzeugtypen zu erkennen.<\/span><\/p><p data-path-to-node=\"25\"><span style=\"font-size: 14pt;\"><b data-path-to-node=\"25\" data-index-in-node=\"0\">Hardware-Einrichtung:<\/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\">Kern:<\/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 (3-Kern-Cluster)<\/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\">K\u00fchlung:<\/b> L\u00fcfterloses Aluminiumgeh\u00e4use (passiv)<\/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\">Strategie f\u00fcr den Einsatz:<\/b> Durch die Nutzung der Multi-Core-NPU haben wir 5-6 Streams pro NPU-Kern zugewiesen. Wir haben eine beschnittene <b data-path-to-node=\"27\" data-index-in-node=\"110\">MobileNet-SSD<\/b> Architektur, die auf die Lokalisierung von Nummernschildern zugeschnitten ist.<\/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=\"Industrielle Inspektion\" 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\">Die Daten:<\/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\">Durchsatz:<\/b> 18-20 FPS pro Kanal \u00fcber alle 16 Kan\u00e4le.<\/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\">Stromverbrauch:<\/b> Das gesamte System verbrauchte unter voller AI-Last nur 7,4 W.<\/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\">Vergleich:<\/b> Eine \u00e4hnliche <b data-path-to-node=\"28,2,0\" data-index-in-node=\"22\">international bekannter Fall<\/b> Die Verwendung des NVIDIA Jetson Orin NX (25 W) lieferte h\u00f6here FPS, allerdings bei dreifachen Hardwarekosten und deutlich h\u00f6herer W\u00e4rmeabgabe, die eine aktive K\u00fchlung erforderlich gemacht h\u00e4tte - ein Fehler in staubigen Schaltschr\u00e4nken am Stra\u00dfenrand.<\/span><\/p><\/li><\/ul><p data-path-to-node=\"29\"><span style=\"font-size: 14pt;\">Dieses Projekt hat bewiesen, dass die 6 TOPS des RK3588 f\u00fcr verteilte Edge-Knoten der \"Sweet Spot\" f\u00fcr ein ausgewogenes Verh\u00e4ltnis zwischen Multi-Stream-F\u00e4higkeit und thermischer Zuverl\u00e4ssigkeit sind.<\/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\">Sind 6 TOPS genug? Eine endg\u00fcltige Entscheidungsmatrix<\/h2><p data-path-to-node=\"31\"><span style=\"font-size: 14pt;\">Bevor Sie sich f\u00fcr Ihre Hardware entscheiden, sollten Sie sich diese drei Fragen stellen:<\/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\">Ist Ihr Modell CNN-basiert?<\/b> Wenn ja (YOLO, ResNet, SSD), ist die RK3588 ausgezeichnet.<\/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\">Wie hoch ist Ihr Latenzbudget?<\/b> Wenn Sie &lt;10ms f\u00fcr ein komplexes YOLOv8l-Modell ben\u00f6tigen, brauchen Sie m\u00f6glicherweise einen 20+ TOPS-Beschleuniger wie den <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\">Muss Ihr System mehr k\u00f6nnen als KI?<\/b> Wenn Sie auch 4K-Videos kodieren oder einen Webserver betreiben m\u00fcssen, ist der RK3588 mit seiner Octa-Core-CPU und der 8K-VPU den \"reinen\" KI-Chips \u00fcberlegen.<\/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;\">Wenn Sie sich nicht sicher sind, ob 6 TOPS f\u00fcr Ihr Modell ausreichend sind, k\u00f6nnen Sie <b data-path-to-node=\"30\" data-index-in-node=\"62\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/de\/contact-us\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQiAE\">einen Kostenvoranschlag und eine technische Bewertung anfordern<\/a><\/b> von unserem Ingenieurteam.<\/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\">Schlussfolgerung: Die Leistung der RK3588 NPU optimal nutzen<\/h2><p data-path-to-node=\"4\"><span style=\"font-size: 14pt;\">Die <b data-path-to-node=\"4\" data-index-in-node=\"4\">RK3588 NPU-Leistung<\/b> ist ein entscheidender Faktor f\u00fcr das industrielle Edge-Computing, aber nur in Verbindung mit dem richtigen technischen Ansatz. W\u00e4hrend \"6 TOPS\" die Schlagzeile ist, liegt der wahre Wert in der F\u00e4higkeit des Chips, Hochgeschwindigkeitsdaten zu verarbeiten. <b data-path-to-node=\"4\" data-index-in-node=\"236\">INT8<\/b> bei gleichzeitiger Verwaltung von 8K-Videostr\u00f6men und komplexen E\/A-Aufgaben.<\/span><\/p><p data-path-to-node=\"5\"><span style=\"font-size: 14pt;\">F\u00fcr Ingenieure f\u00fchrt der Weg zum Erfolg \u00fcber eine solide <b data-path-to-node=\"5\" data-index-in-node=\"53\">RKNN-Toolkit2 Inferenz-Benchmark<\/b> Strategie, die sich auf hochwertige Quantisierung und Operator-Optimierung konzentriert. Projektmanagern bietet er die M\u00f6glichkeit, Ergebnisse auf \"Jetson-Niveau\" zu einem deutlich g\u00fcnstigeren Preis und mit einem geringeren Energiebudget zu erzielen. Ganz gleich, ob Sie ein AOI-System f\u00fcr eine Fabrik oder einen Multistream-Verkehrsmonitor bauen, die RK3588 bietet den n\u00f6tigen Spielraum f\u00fcr die n\u00e4chste Generation <b data-path-to-node=\"5\" data-index-in-node=\"464\">Edge-KI-Eins\u00e4tze<\/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\">FAQ<\/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=\"Akkordeon. \u00d6ffnen Sie Links mit Enter oder Space, schlie\u00dfen Sie sie mit Escape und navigieren Sie mit den Pfeiltasten.\">\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\"> F: Kann ich Llama-3 auf dem RK3588 betreiben? <\/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;\">A: Gro\u00dfe Sprachmodelle (8B+) \u00fcbersteigen die effiziente Speicherverwaltung der NPU. Allerdings laufen 1B-2B-Parameter-Modelle wie TinyLlama oder Qwen-1.8B effektiv mit ~15 Token\/s unter Verwendung der <a class=\"ng-star-inserted\" href=\"https:\/\/github.com\/airockchip\/rknn-llm\" target=\"_blank\" rel=\"noopener\" data-hveid=\"48\">RKLLM-Laufzeit<\/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\"> F: Unterst\u00fctzt die RK3588 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;\">A: Ja, durch Konvertierung in ONNX oder direkt durch das RKNN-Toolkit2 TensorFlow-Frontend.<\/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\"> F: Wie verh\u00e4lt sich der NPU bei Hitze in industriellen Umgebungen? <\/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;\">A: Bei voller 6 TOPS-Last tr\u00e4gt die NPU etwa 2-3 W zur Leistungsaufnahme des SoC bei. Bei einer <b data-path-to-node=\"10\" data-index-in-node=\"148\">ieeker<\/b> l\u00fcfterlosen Industriegeh\u00e4use kann er bei Umgebungstemperaturen von bis zu 60\u00b0C seine Spitzenleistung aufrechterhalten.<\/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\"> F: Wo kann ich technische Unterst\u00fctzung f\u00fcr ieeker-Boards erhalten? <\/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;\">A: Besuchen Sie unsere <b data-path-to-node=\"34\" data-index-in-node=\"69\"><a class=\"ng-star-inserted\" href=\"https:\/\/ieeker.com\/de\/support\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwjqroT_8bWUAxUAAAAAHQAAAAAQiQE\">technische unterst\u00fctzung f\u00fcr ieeker-platten<\/a><\/b> Seite f\u00fcr Dokumentation und RKNN-Skripte.<\/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\">Referenzen<\/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\">Technische Definitionen:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"49\">TOPS (Tera-Operationen pro Sekunde) - 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\">Offizielle Toolchain:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"50\">Rockchip RKNN-Toolkit2 GitHub-Repository<\/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\">Industrielle Normen:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"51\">Embedded Vision Alliance - Benchmarking von Prozessoren<\/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\">Leistungsvergleiche:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"52\">CNX Software - Rockchip RK3588 AI Leistungstests<\/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\">Vergleichsdaten:<\/b> <a class=\"ng-star-inserted\" target=\"_blank\" rel=\"noopener\" data-hveid=\"53\">NVIDIA Jetson Orin Nano Technische Daten<\/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>Die Leistung der RK3588 NPU entscheidet dar\u00fcber, ob Ihr Edge-Ger\u00e4t KI-Inferenzen in Echtzeit ohne einen dedizierten Grafikprozessor verarbeiten kann. F\u00fcr die meisten industriellen Bildverarbeitungsaufgaben liefert die 6 TOPS NPU \u00fcber 50 FPS f\u00fcr YOLOv5s und ersetzt effektiv NVIDIA Jetson-Module der Einstiegsklasse durch ein ausgewogenes Verh\u00e4ltnis von Kosten, Leistung und I\/O-Integration. Wichtigste Erkenntnisse Leistungsbenchmark: RK3588 erreicht 54+ FPS bei [...]<\/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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