{"id":1617,"date":"2023-06-27T22:25:03","date_gmt":"2023-06-27T19:25:03","guid":{"rendered":"https:\/\/aivolga.com\/?page_id=1617"},"modified":"2024-05-22T10:38:24","modified_gmt":"2024-05-22T07:38:24","slug":"ve1076-m-2-a-e-key-card","status":"publish","type":"page","link":"https:\/\/aivolga.com\/?page_id=1617","title":{"rendered":"VE1076 M.2 A+E Key Card"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1617\" class=\"elementor elementor-1617\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-72eab73 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"72eab73\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-f288473 elementor-invisible\" data-id=\"f288473\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;animation&quot;:&quot;fadeInLeft&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-64652e4 elementor-widget elementor-widget-heading\" data-id=\"64652e4\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\"><span style=\"color:#12b39b\">VE1076 M.2 A+E Key Card<\/span><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be2458e elementor-widget elementor-widget-heading\" data-id=\"be2458e\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Overview<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6fd38ade elementor-widget elementor-widget-text-editor\" data-id=\"6fd38ade\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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\tThe VE1076 M.2 A+E key card enables high-performance, yet power-efficient AI inference for edge devices and edge servers. The M.2 card\u2019s compact form-factor and popularity makes integration into many different systems a straightforward task. The VE1076 is designed with the V1076 Volga AMP\u2122 which is arranged in an array of AMP tiles each featuring a Volga Analog Compute Engine (Volga ACE\u2122). The VE1076 is ideal for processing deep neural network (DNN) models in a variety of applications, including video surveillance, industrial machine vision, drone, AR\/VR, and edge servers.\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<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-6921bbc elementor-invisible\" data-id=\"6921bbc\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;animation&quot;:&quot;fadeInLeft&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0de4434 elementor-widget elementor-widget-image\" data-id=\"0de4434\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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\" src=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Image-2-1-1.jpg\" title=\"Image-2-1.jpg\" alt=\"Image-2-1.jpg\" loading=\"lazy\" \/>\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\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9cbe3f3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9cbe3f3\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-25db827 elementor-hidden-mobile\" data-id=\"25db827\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-284a227 elementor-hidden-desktop elementor-hidden-tablet elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"284a227\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cf01a5e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cf01a5e\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-aa4d312 elementor-invisible\" data-id=\"aa4d312\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;animation&quot;:&quot;fadeInLeft&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c3f4db2 elementor-widget elementor-widget-heading\" data-id=\"c3f4db2\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\"><span>Features<\/span><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f633f28 elementor-widget elementor-widget-text-editor\" data-id=\"f633f28\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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<ul style=\"padding-left: 15px;\"><li style=\"text-align: left;\">V1076 Volga AMP\u2122 with support for up to 80M weights on-chip<\/li><li style=\"text-align: left;\">No external DRAM required<\/li><li style=\"text-align: left;\">SMBus for EEPROM and PMIC access<\/li><li style=\"text-align: left;\">Pre-qualified networks including object detectors, classifiers, pose estimators, with more being added<\/li><li style=\"text-align: left;\">OS Support: Ubuntu, NVIDIA L4T, and Windows (future release)<\/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<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-84abff1 elementor-invisible\" data-id=\"84abff1\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;animation&quot;:&quot;fadeInLeft&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ec72c26 elementor-widget elementor-widget-text-editor\" data-id=\"ec72c26\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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<ul style=\"padding-left: 15px;\"><li style=\"text-align: left;\">Model parameters stored and matrix operations executed on-chip by AMP tiles<\/li><li style=\"text-align: left;\">2-lane PCIe 2.1 for up to 1GB\/s bandwidth<\/li><li style=\"text-align: left;\">Support for standard frameworks, including PyTorch, TensorFlow 2.0, and Caffe<\/li><li style=\"text-align: left;\">Small 22mm x 30mm form factor<\/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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-59786a5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"59786a5\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-adc0641 elementor-hidden-mobile\" data-id=\"adc0641\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2f185cd elementor-hidden-desktop elementor-hidden-tablet elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"2f185cd\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-977b95f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"977b95f\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-b5668eb elementor-invisible\" data-id=\"b5668eb\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;animation&quot;:&quot;fadeInLeft&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1569208 elementor-widget elementor-widget-heading\" data-id=\"1569208\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\"><span>Workflow<\/span><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd6ab4f elementor-widget elementor-widget-text-editor\" data-id=\"bd6ab4f\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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>DNN models developed in standard frameworks such as Pytorch, Caffe, and TensorFlow are implemented and deployed on the Volga Analog Matrix Processor (Volga AMPTM) using Volga\u2019s AI software workflow. Models are optimized, quantized from FP32 to INT8, and then retrained for the Volga Analog Compute Engine (Volga ACETM) prior to being processed through Volga\u2019s powerful graph compiler. Resultant binaries and model weights are then programmed into the Volga AMP for inference. Pre-qualified models are also available for developers to quickly evaluate the Volga AMP solution.<\/p>\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<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-df43a85 elementor-invisible\" data-id=\"df43a85\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;animation&quot;:&quot;fadeInLeft&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4feb655 elementor-widget elementor-widget-image\" data-id=\"4feb655\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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\t<a href=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/VolgaAI-Software-Flow.jpg\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"VolgaAI-Software-Flow\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTQ4NSwidXJsIjoiaHR0cHM6XC9cL2Fpdm9sZ2EuY29tXC93cC1jb250ZW50XC91cGxvYWRzXC8yMDIzXC8wNlwvVm9sZ2FBSS1Tb2Z0d2FyZS1GbG93LmpwZyJ9\">\n\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/VolgaAI-Software-Flow.jpg\" title=\"VolgaAI-Software-Flow\" alt=\"VolgaAI-Software-Flow\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t<\/a>\n\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\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3f9166df elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3f9166df\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1c7d6ae3 elementor-hidden-mobile\" data-id=\"1c7d6ae3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-362cc7c8 elementor-hidden-desktop elementor-hidden-tablet elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"362cc7c8\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c7f4325 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c7f4325\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6f765ad elementor-invisible\" data-id=\"6f765ad\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;animation&quot;:&quot;fadeInLeft&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9ac47d7 elementor-widget elementor-widget-heading\" data-id=\"9ac47d7\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">DNN Model Library<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7bfd597 elementor-widget elementor-widget-text-editor\" data-id=\"7bfd597\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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\tVolga provides a library of pre-qualified DNN models for the most popular AI use cases. The DNN models have been optimized to take advantage of the high-performance and low-power capabilities of the Volga Analog Matrix Processor (Volga AMPTM). Developers can focus on model performance and end-application integration instead of the time-consuming model development and training process. Available pre-qualified DNN models include:\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0e3b034 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0e3b034\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;ekit_has_onepagescroll_dot&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-6d70784\" data-id=\"6d70784\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f7dc690 elementor-widget elementor-widget-image\" data-id=\"f7dc690\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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=\"521\" src=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Object_detector_board_1-1024x667-1.jpg\" class=\"attachment-large size-large wp-image-1514\" alt=\"\" srcset=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Object_detector_board_1-1024x667-1.jpg 1024w, https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Object_detector_board_1-1024x667-1-300x195.jpg 300w, https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Object_detector_board_1-1024x667-1-768x500.jpg 768w\" 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-5341239 elementor-widget elementor-widget-text-editor\" data-id=\"5341239\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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 style=\"text-align: center;\"><span style=\"color: #000000;\">Object Detection and Classification YOLOv3, YOLOv5, ResNet-50, ResNet-18<\/span><\/p>\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<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-8754990\" data-id=\"8754990\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-42bf9cf elementor-widget elementor-widget-image\" data-id=\"42bf9cf\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/downtown_detector-898x1024-1.jpg\" title=\"downtown_detector-898&#215;1024\" alt=\"Object Detection and Classification YOLOv3, YOLOv5, ResNet-50, ResNet-18\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-b8db95a elementor-widget elementor-widget-text-editor\" data-id=\"b8db95a\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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 style=\"text-align: center;\"><span style=\"color: #000000;\">Object Detection and Classification YOLOv3, YOLOv5, ResNet-50, ResNet-18<\/span><\/p>\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<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-9aa849f\" data-id=\"9aa849f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a002cac elementor-widget elementor-widget-image\" data-id=\"a002cac\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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=\"635\" src=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Room-Segmentation_v2-1024x813-1.jpg\" class=\"attachment-large size-large wp-image-1512\" alt=\"\" srcset=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Room-Segmentation_v2-1024x813-1.jpg 1024w, https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Room-Segmentation_v2-1024x813-1-300x238.jpg 300w, https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Room-Segmentation_v2-1024x813-1-768x610.jpg 768w\" 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-063c1fe elementor-widget elementor-widget-text-editor\" data-id=\"063c1fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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 class=\"Standard\" style=\"text-align: center;\"><span lang=\"EN-GB\" style=\"color: #000000;\">Scene Segmentation SegNet<\/span><\/p>\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<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-6a4d4b6\" data-id=\"6a4d4b6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2d2b4a5 elementor-widget elementor-widget-image\" data-id=\"2d2b4a5\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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\" src=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Human-pose-estimator-1024x683-2.jpg\" class=\"attachment-large size-large wp-image-1515\" alt=\"\" srcset=\"https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Human-pose-estimator-1024x683-2.jpg 1024w, https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Human-pose-estimator-1024x683-2-300x200.jpg 300w, https:\/\/aivolga.com\/wp-content\/uploads\/2023\/06\/Human-pose-estimator-1024x683-2-768x512.jpg 768w\" 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-64a4cf1 elementor-widget elementor-widget-text-editor\" data-id=\"64a4cf1\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;ekit_we_effect_on&quot;:&quot;none&quot;}\" 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 class=\"Standard\" style=\"text-align: center;\"><span lang=\"EN-GB\" style=\"color: #000000;\">Human Pose Estimator OpenPose Body25<\/span><\/p><p style=\"text-align: center;\"><span style=\"color: #000000;\">\u00a0<\/span><\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>VE1076 M.2 A+E Key Card Overview The VE1076 M.2 A+E key card enables high-performance, yet power-efficient AI inference for edge devices and edge servers. The M.2 card\u2019s compact form-factor and popularity makes integration into many different systems a straightforward task. The VE1076 is designed with the V1076 Volga AMP\u2122 which is arranged in an array [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":486,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1617","page","type-page","status-publish","hentry"],"lang":"en","translations":{"en":1617,"ru":1615},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/aivolga.com\/index.php?rest_route=\/wp\/v2\/pages\/1617","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aivolga.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/aivolga.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/aivolga.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aivolga.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1617"}],"version-history":[{"count":29,"href":"https:\/\/aivolga.com\/index.php?rest_route=\/wp\/v2\/pages\/1617\/revisions"}],"predecessor-version":[{"id":3885,"href":"https:\/\/aivolga.com\/index.php?rest_route=\/wp\/v2\/pages\/1617\/revisions\/3885"}],"up":[{"embeddable":true,"href":"https:\/\/aivolga.com\/index.php?rest_route=\/wp\/v2\/pages\/486"}],"wp:attachment":[{"href":"https:\/\/aivolga.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1617"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}