{"id":4812,"date":"2025-06-24T04:04:11","date_gmt":"2025-06-24T04:04:11","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/06\/24\/new-framework-reduces-memory-usage-and-boosts-energy-efficiency-for-large-scale-ai-graph-analysis\/"},"modified":"2025-06-24T04:04:11","modified_gmt":"2025-06-24T04:04:11","slug":"new-framework-reduces-memory-usage-and-boosts-energy-efficiency-for-large-scale-ai-graph-analysis","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/06\/24\/new-framework-reduces-memory-usage-and-boosts-energy-efficiency-for-large-scale-ai-graph-analysis\/","title":{"rendered":"New framework reduces memory usage and boosts energy efficiency for large-scale AI graph analysis"},"content":{"rendered":"<p>    New framework reduces memory usage and boosts energy efficiency for large-scale AI graph analysis<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the Institute of Science Tokyo, Japan. This breakthrough framework utilizes an innovative cross-partition message quantization technique and a novel training algorithm to significantly reduce memory demands and increase computational and energy efficiency.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><\/p>\n<p> \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/techxplore.com\/news\/2025-06-framework-memory-usage-boosts-energy.html\">Go to techxplore<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>New framework reduces memory usage and boosts energy efficiency for large-scale AI graph analysis BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the Institute of Science Tokyo, Japan. This breakthrough framework utilizes an innovative cross-partition message quantization technique [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[59,45],"tags":[50],"class_list":["post-4812","post","type-post","status-publish","format-standard","hentry","category-hardware","category-techxplore","tag-techxplore"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4812"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=4812"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4812\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=4812"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=4812"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=4812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}