{"id":409,"date":"2020-06-10T19:59:37","date_gmt":"2020-06-10T19:59:37","guid":{"rendered":"http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=409"},"modified":"2020-07-16T15:42:52","modified_gmt":"2020-07-16T15:42:52","slug":"409","status":"publish","type":"post","link":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=409","title":{"rendered":"Infineon Aurix Project 2020"},"content":{"rendered":"\n<p>The main aim of the project is to perform machine learning on the AURIX TC297 TFT board (TC29x B-Step MCU). Relying on the board features and its memory and computation constraints, a study has been done to understand which machine learning regression models could be adapted and run on the device.<\/p>\n\n\n\n<p>The essential board components for this purpose are Ethernet, display, and multicore execution. The board communicates with a Python client installed in the host PC through the Ethernet connection, given its reliability and flexibility. The display is the simplest interface to give a quick view of the available data, without waiting for a download and post-processing of the results. The multicore execution is used to increase the computation performance.<\/p>\n\n\n\n<p>Through the Ethernet connection it is possible to send data and receive the predictions from the models implemented within the board. Communication takes place through the classic TCP\/IP protocol, where the board acts as a server and responds to connection requests from clients (which in the case study are installed in the Host PC, but can be installed on other boards as well). The scatter-plot of the predictions, which is typically used to show the results of regression models, is also plotted on the board screen during the data acquisition.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" width=\"789\" height=\"847\" src=\"http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/wp-content\/uploads\/2020\/06\/general_architecture-1.png\" alt=\"\" class=\"wp-image-411\" srcset=\"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/wp-content\/uploads\/2020\/06\/general_architecture-1.png 789w, https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/wp-content\/uploads\/2020\/06\/general_architecture-1-279x300.png 279w, https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/wp-content\/uploads\/2020\/06\/general_architecture-1-768x824.png 768w, https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/wp-content\/uploads\/2020\/06\/general_architecture-1-750x805.png 750w\" sizes=\"(max-width: 789px) 100vw, 789px\" \/><\/figure>\n\n\n\n<p>For further details you can read the following articles:<br><br><strong>Display: <\/strong><a href=\"http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=380\"><strong>http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=380<\/strong><\/a><br><br><strong>Ethernet: <\/strong><a href=\"http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=381\"><strong>http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=381<\/strong><\/a><br><br><strong>ML: <\/strong><a href=\"http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=371\"><strong>http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=371<\/strong><\/a><br><br><strong>Multi-core: <\/strong><a href=\"http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=379\"><strong>http:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=379<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The main aim of the project is to perform machine learning on the AURIX TC297 TFT board (TC29x B-Step MCU). Relying on the board features and its memory and computation constraints, a study has been done to understand which machine learning regression models could be adapted and run on the device. The essential board components&hellip;<a href=\"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/?p=409\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Infineon Aurix Project 2020<\/span><\/a><\/p>\n","protected":false},"author":23,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=\/wp\/v2\/posts\/409"}],"collection":[{"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=409"}],"version-history":[{"count":7,"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=\/wp\/v2\/posts\/409\/revisions"}],"predecessor-version":[{"id":443,"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=\/wp\/v2\/posts\/409\/revisions\/443"}],"wp:attachment":[{"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=409"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=409"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cas.polito.it\/IFX-AURIX@PoliTo-University\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=409"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}