{"id":15151,"date":"2022-06-20T13:00:36","date_gmt":"2022-06-20T11:00:36","guid":{"rendered":"https:\/\/quantum.com.pl\/joint-presentation-of-lauterbach-and-sifive-at-embedded-world-2022\/"},"modified":"2022-06-20T12:50:40","modified_gmt":"2022-06-20T10:50:40","slug":"joint-presentation-of-lauterbach-and-sifive-at-embedded-world-2022","status":"publish","type":"post","link":"https:\/\/quantum.com.pl\/en\/joint-presentation-of-lauterbach-and-sifive-at-embedded-world-2022\/","title":{"rendered":"JOINT PRESENTATION OF LAUTERBACH AND SiFive AT EMBEDDED WORLD 2022"},"content":{"rendered":"

Lauterbach and SiFive to Demonstrate Multi-core RISC-V Vector Processor Debug at Embedded World 2022<\/strong><\/p>\n

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The SiFive\u00ae Intelligence™ X280 is a multi-core capable RISC-V processor with vector extensions and is optimized for AI\/ML compute at the edge. In addition to ML inferencing, it is ideal for applications requiring high-throughput, single-thread performance, while under power constraints.<\/p>\n

With its configurability, the Lauterbach multi-core debugger for 64-bit RISC-V cores offers ideal support for all multi-core\/multi-cluster configurations of the SiFive X280 core, as well as for all available ISA extensions and Linux debugging. In addition, Lauterbach offers a trace extension for recording of parallel RISC-V Nexus trace. Thus, Linux-aware detailed information about the program execution is available for all cores\/clusters. This allows complex runtime errors to be identified quickly and runtime optimizations to be performed easily.<\/p>\n

Come see us demonstrate this live in the Lauterbach\u00a0booth #4-210<\/b>\u00a0at the\u00a0embedded world 2022<\/b>.
\nThe embedded world 2022 will take place on 21 – 23 June 2022 in Nuremberg, Germany.<\/p>\n","protected":false},"excerpt":{"rendered":"

Lauterbach and SiFive to Demonstrate Multi-core RISC-V Vector Processor Debug at Embedded World 2022     The SiFive\u00ae Intelligence™ X280 is a multi-core capable RISC-V processor with vector extensions and is optimized for AI\/ML compute at the edge. In addition to ML inferencing, it is ideal for applications requiring high-throughput, single-thread performance, while under power […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[1653],"tags":[],"class_list":["post-15151","post","type-post","status-publish","format-standard","hentry","category-news-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/posts\/15151","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/comments?post=15151"}],"version-history":[{"count":4,"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/posts\/15151\/revisions"}],"predecessor-version":[{"id":15155,"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/posts\/15151\/revisions\/15155"}],"wp:attachment":[{"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/media?parent=15151"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/categories?post=15151"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/quantum.com.pl\/en\/wp-json\/wp\/v2\/tags?post=15151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}