{"version":"1.0","provider_name":"Plunify","provider_url":"https:\/\/www.plunify.com\/en","author_name":"Plunify","author_url":"https:\/\/www.plunify.com\/en","title":"Whitepapers &mdash; Plunify","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\"><a href=\"https:\/\/www.plunify.com\/en\/whitepapers\/\">Whitepapers<\/a><\/blockquote>\n<script type='text\/javascript'>\n<!--\/\/--><![CDATA[\/\/><!--\n\t\t\/*! This file is auto-generated *\/\n\t\t!function(d,l){\"use strict\";var e=!1,n=!1;if(l.querySelector)if(d.addEventListener)e=!0;if(d.wp=d.wp||{},!d.wp.receiveEmbedMessage)if(d.wp.receiveEmbedMessage=function(e){var t=e.data;if(t)if(t.secret||t.message||t.value)if(!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var r,i,a,s=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),n=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),o=new RegExp(\"^https?:$\",\"i\"),c=0;c<n.length;c++)n[c].style.display=\"none\";for(c=0;c<s.length;c++)if(r=s[c],e.source===r.contentWindow){if(r.removeAttribute(\"style\"),\"height\"===t.message){if(1e3<(a=parseInt(t.value,10)))a=1e3;else if(~~a<200)a=200;r.height=a}if(\"link\"===t.message)if(i=l.createElement(\"a\"),a=l.createElement(\"a\"),i.href=r.getAttribute(\"src\"),a.href=t.value,o.test(a.protocol))if(a.host===i.host)if(l.activeElement===r)d.top.location.href=t.value}}},e)d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",t,!1),d.addEventListener(\"load\",t,!1);function t(){if(!n){n=!0;for(var e,t,r=-1!==navigator.appVersion.indexOf(\"MSIE 10\"),i=!!navigator.userAgent.match(\/Trident.*rv:11\\.\/),a=l.querySelectorAll(\"iframe.wp-embedded-content\"),s=0;s<a.length;s++){if(!(e=a[s]).getAttribute(\"data-secret\"))t=Math.random().toString(36).substr(2,10),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t);if(r||i)(t=e.cloneNode(!0)).removeAttribute(\"security\"),e.parentNode.replaceChild(t,e)}}}}(window,document);\n\/\/--><!]]>\n<\/script><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.plunify.com\/en\/whitepapers\/embed\/\" width=\"600\" height=\"338\" title=\"&#8220;Whitepapers&#8221; &#8212; Plunify\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe>","description":"[vc_row row_type=&#8221;row&#8221; stretch_row_type=&#8221;no&#8221; css=&#8221;.vc_custom_1484622607279{margin-top: 20px !important;}&#8221;][vc_column][vc_column_text css=&#8221;.vc_custom_1497490595069{padding-top: 10px !important;}&#8221;] Whitepapers [\/vc_column_text][\/vc_column][\/vc_row][vc_row row_type=&#8221;row&#8221; stretch_row_type=&#8221;no&#8221;][vc_column width=&#8221;5\/6&#8243;][vc_column_text] Transformational ML-Based Approach to Tackle Severe FPGA Placement and Routing Failures Placement and routing failures during the FPGA backend flow are frequent with many FPGA designs especially when these involve multiple physical constraints. This paper introduces the Machine-Learning-based approach implemented in [&hellip;]"}