{"id":3368,"date":"2018-05-22T09:57:59","date_gmt":"2018-05-22T09:57:59","guid":{"rendered":"https:\/\/support.plunify.com\/en\/?p=3368"},"modified":"2018-08-28T03:22:31","modified_gmt":"2018-08-28T03:22:31","slug":"intime-timing-closure-methodology-vivado","status":"publish","type":"post","link":"https:\/\/support.plunify.com\/en\/2018\/05\/22\/intime-timing-closure-methodology-vivado\/","title":{"rendered":"Whitepaper: InTime Timing Closure Methodology for Vivado"},"content":{"rendered":"

Introduction<\/h2>\n

The InTime<\/a> Timing Closure Methodology is a set of best practices and guidelines to determine the best build parameters under the condition that the design is currently immutable, i.e. you cannot change your RTL or constraints. InTime uses machine learning principles to achieve timing closure or optimization, treating the FPGA synthesis and place-and-route tools as black boxes and analyzing design performance across a whole range of build parameter variations.<\/p>\n

Under the InTime Timing Closure Methodology, the build process is no longer one designer-to-one-machine operation. Instead, it is a systematic series of calculated steps done by one or many designers on multiple build machines. From the resulting analysis, InTime deduces and recommends sets of good build parameters aimed at improving design performance.<\/p>\n

\"intime-learningcycle-en\"

Figure 1: InTime Learning Lifecycle<\/p><\/div>\n

The guidelines in this document will help you achieve your performance goals in the minimum number of compilations and fastest turnaround time possible.<\/p>\n

Figure 2 is a flowchart that explains how InTime optimizes Vivado Designs. The white boxes are InTime's unique machine learning approaches called \u201cRecipe\u201d<\/a>.<\/p>\n

\"InTime-flow-en\"

Figure 2: InTime Optimization Flow for Vivado Designs<\/p><\/div>\n

Topics covered in this whitepaper<\/h2>\n