{"id":1328,"date":"2021-03-09T19:01:04","date_gmt":"2021-03-09T18:01:04","guid":{"rendered":"https:\/\/apegate.roma1.infn.it\/?page_id=1328"},"modified":"2021-06-12T16:41:41","modified_gmt":"2021-06-12T14:41:41","slug":"apeiron","status":"publish","type":"page","link":"https:\/\/apegate.roma1.infn.it\/?page_id=1328","title":{"rendered":"APEIRON"},"content":{"rendered":"\t\t
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Abstract Processing Environment for Intelligent Read-Out systems based on Neural networks<\/h3><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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APEIRON\u00a0<\/span>is a<\/span>\u00a0<\/span>framework<\/span>\u00a0<\/span>consisting\u00a0of a\u00a0<\/span>gener<\/span>ic\u00a0<\/span>architecture\u00a0<\/span>of\u00a0<\/span>a<\/span>\u00a0<\/span>distributed\u00a0<\/span>computing platform<\/span>\u00a0<\/span>and\u00a0<\/span>its\u00a0<\/span>complete\u00a0<\/span>software stack, from device driver to high level programming model. The framework has been conceived<\/span>\u00a0<\/span>to\u00a0<\/span>prototype and deploy\u00a0<\/span>intelligent\u00a0<\/span>systems\u00a0<\/span>in<\/span>\u00a0<\/span>the\u00a0<\/span>T<\/span>rigger and Data Acquisition\u00a0<\/span>(TDAQ)\u00a0<\/span>c<\/span>ontext\u00a0<\/span>of\u00a0<\/span>High Energy Physics\u00a0<\/span>(HEP)\u00a0<\/span><\/span>experiments<\/span>.<\/span><\/span>\u00a0<\/span>It leverages\u00a0<\/span>a high<\/span>\u00a0<\/span>degree<\/span>\u00a0<\/span>of<\/span>\u00a0<\/span>abstraction<\/span>,\u00a0<\/span>modern\u00a0<\/span>electronic\u00a0<\/span>technolog<\/span>y<\/span>\u00a0<\/span>and\u00a0<\/span>modern\u00a0<\/span>coding approach\u00a0<\/span>to<\/span>\u00a0<\/span>efficiently\u00a0<\/span>develop\u00a0<\/span>solutions<\/span>\u00a0<\/span>that implements<\/span>\u00a0<\/span>neural<\/span>\u00a0<\/span>network<\/span>s\u00a0<\/span>in<\/span>\u00a0<\/span>a\u00a0<\/span>low<\/span>er<\/span>–<\/span>level trigger\u00a0<\/span>module<\/span>\u00a0<\/span>of<\/span>\u00a0traditional\u00a0<\/span>setups\u00a0<\/span>or in a\u00a0<\/span>data reduction<\/span>\u00a0<\/span>stage\u00a0<\/span>in triggerless<\/span>\u00a0<\/span>systems<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n<\/div>\n

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An\u00a0<\/span>A<\/span>PEIRON\u00a0<\/span>architecture\u00a0<\/span>is\u00a0<\/span>made\u00a0of\u00a0<\/span>several\u00a0input\u00a0data<\/span>\u00a0channels<\/span>\u00a0<\/span>that in\u00a0<\/span>TDAQ\u00a0<\/span>for HEP\u00a0<\/span>correspond to detector<\/span>s<\/span>\u00a0and subdetector modules<\/span>. Input channels are connected to\u00a0<\/span>several multi-processors streaming\u00a0<\/span>layers<\/span>\u00a0<\/span>with\u00a0<\/span>the\u00a0<\/span>output\u00a0<\/span>layer\u00a0<\/span>being\u00a0<\/span>a trigger processor\u00a0<\/span>or\u00a0<\/span>a storage server depending on the application.<\/span>\u00a0<\/span>The platform is modular, scalable and has\u00a0<\/span>a\u00a0<\/span>configurable topology<\/span>\u00a0as its strong point<\/span>s.<\/span>\u00a0<\/span>The configurable topology\u00a0<\/span>allow<\/span>s<\/span>\u00a0for\u00a0<\/span>arbitrary\u00a0<\/span>combinations of data\u00a0<\/span>to flow\u00a0<\/span>through\u00a0the computation<\/span>al<\/span>\u00a0network across the different processing layers<\/span>.\u00a0<\/span>The latter can be any\u00a0<\/span>sets of heterogeneous devices<\/span>,<\/span>\u00a0<\/span>namely\u00a0<\/span>FPGA, CPU<\/span>\u00a0and<\/span>\u00a0<\/span>GPU<\/span>,<\/span>\u00a0<\/span>conveniently defined at\u00a0<\/span>early\u00a0<\/span>stage of\u00a0<\/span>design\u00a0<\/span>to take advantage of\u00a0<\/span>the\u00a0<\/span>peculiar hardware\u00a0<\/span>characteristics\u00a0<\/span>of each one\u00a0<\/span>in terms of\u00a0<\/span>timing, memory and computing power<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n<\/div>\n

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Hardware abstraction and optimal application mapping\u00a0<\/span>are<\/span>\u00a0based\u00a0<\/span>on two key elements:<\/span><\/span>\u00a0<\/span><\/p>\n<\/div>\n

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    A distributed dataflow programming model based on\u00a0<\/span>Kahn Process Networks<\/span>\u00a0(KPN)<\/span><\/span>\u00a0<\/span><\/p>\n<\/li>\n

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    The adoption of modern\u00a0<\/span>High Level Synthesis\u00a0<\/span>design\u00a0<\/span>tool<\/span>s<\/span>\u00a0for FPGA<\/span>.<\/span>\u00a0<\/span><\/span>\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<\/div>\n

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    Thus,<\/span>\u00a0a\u00a0<\/span>computation\u00a0<\/span>can be represented as\u00a0<\/span>a\u00a0<\/span>direct graph in which the vertices are computa<\/span>t<\/span>ion<\/span>al tasks to be executed\u00a0<\/span>on platform nodes\u00a0<\/span>and the oriented edges are communication channels\u00a0<\/span>for data streaming<\/span>\u00a0between tasks.<\/span>\u00a0<\/span>U<\/span>sing high<\/span>-level language (C\/C++)\u00a0<\/span><\/span>implies\u00a0<\/span>code\u00a0<\/span>portability and an adequate level of abstraction<\/span>\u00a0<\/span>from the actual device that is going to implement a task.\u00a0<\/span><\/span>In\u00a0<\/span>addition,<\/span>\u00a0<\/span>d<\/span>irect graph<\/span>s<\/span>\u00a0<\/span>representation\u00a0<\/span>let\u00a0<\/span>the application to be simulated\u00a0<\/span>in advance so that\u00a0the\u00a0<\/span>optimal partitioning\u00a0<\/span>can\u00a0<\/span>be\u00a0<\/span>defined<\/span>\u00a0<\/span>even before the hardware is practically available.<\/span>\u00a0Another byproduct of this approach is\u00a0<\/span>that different\u00a0<\/span>mappings for a single application\u00a0<\/span>can be\u00a0<\/span>simulated<\/span>\u00a0speeding up the\u00a0<\/span>prototyping<\/span>\u00a0phase.<\/span><\/span>\u00a0<\/span><\/p>\n<\/div>\n

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    APEIRON framework is flexible<\/span>\u00a0and\u00a0<\/span>can be used\u00a0<\/span>in the new experimental scenario<\/span>s\u00a0<\/span>characterized by\u00a0<\/span>elevated<\/span>\u00a0particle rates and\u00a0<\/span>huge\u00a0<\/span>number of channels.<\/span>\u00a0<\/span>In the case of triggered\u00a0<\/span>systems,<\/span>\u00a0we<\/span>\u00a0aim<\/span>\u00a0at<\/span>\u00a0<\/span>developing and deploying\u00a0<\/span>innovative algorithms based on\u00a0<\/span><\/span>d<\/span>eep\u00a0<\/span><\/span>and\u00a0<\/span>s<\/span>piking\u00a0<\/span><\/span>neural network<\/span>s<\/span>\u00a0to improve\u00a0<\/span>trigger\u00a0<\/span>efficiency in\u00a0<\/span>online system<\/span>s<\/span>.<\/span>\u00a0In fact, the architecture and the\u00a0<\/span>programming model are conceived to support the distributed implementation of\u00a0<\/span>machine learning calculations\u00a0<\/span>by mapping different layers and subnets<\/span>\u00a0on interconnected processing elements. A first use case of\u00a0<\/span>APEIRON is a FPGA base<\/span>d\u00a0<\/span>n<\/span>eural network\u00a0<\/span>for\u00a0<\/span>implementing partial particle identification in\u00a0<\/span>the\u00a0<\/span>low-level trigger of NA62 experiment at CERN. In a second case\u00a0<\/span>we\u2019re going to use APEIRON framework in a\u00a0streaming readout system for the\u00a0<\/span>online\u00a0<\/span>data reduction<\/span>\u00a0and permanent storage of the data frames.<\/span><\/span>\u00a0<\/span><\/p>\n<\/div>\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":"

    Abstract Processing Environment for Intelligent Read-Out systems based on Neural networks APEIRON\u00a0is a\u00a0framework\u00a0consisting\u00a0of a\u00a0generic\u00a0architecture\u00a0of\u00a0a\u00a0distributed\u00a0computing platform\u00a0and\u00a0its\u00a0complete\u00a0software stack, from device driver to high level programming model. The framework has been conceived\u00a0to\u00a0prototype and deploy\u00a0intelligent\u00a0systems\u00a0in\u00a0the\u00a0Trigger and Data Acquisition\u00a0(TDAQ)\u00a0context\u00a0of\u00a0High Energy Physics\u00a0(HEP)\u00a0experiments.\u00a0It leverages\u00a0a high\u00a0degree\u00a0of\u00a0abstraction,\u00a0modern\u00a0electronic\u00a0technology\u00a0and\u00a0modern\u00a0coding approach\u00a0to\u00a0efficiently\u00a0develop\u00a0solutions\u00a0that implements\u00a0neural\u00a0networks\u00a0in\u00a0a\u00a0lower-level trigger\u00a0module\u00a0of\u00a0traditional\u00a0setups\u00a0or in a\u00a0data reduction\u00a0stage\u00a0in triggerless\u00a0systems.\u00a0 An\u00a0APEIRON\u00a0architecture\u00a0is\u00a0made\u00a0of\u00a0several\u00a0input\u00a0data\u00a0channels\u00a0that in\u00a0TDAQ\u00a0for HEP\u00a0correspond to detectors\u00a0and subdetector modules. Input channels are […]<\/p>\n","protected":false},"author":12,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-1328","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=\/wp\/v2\/pages\/1328","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1328"}],"version-history":[{"count":22,"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=\/wp\/v2\/pages\/1328\/revisions"}],"predecessor-version":[{"id":1397,"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=\/wp\/v2\/pages\/1328\/revisions\/1397"}],"wp:attachment":[{"href":"https:\/\/apegate.roma1.infn.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1328"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}