{"id":215692,"date":"2025-06-10T10:03:24","date_gmt":"2025-06-10T15:03:24","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/06\/1-comment-on-maryland-u-google-introduce-lilnetx-simultaneously-optimizing-dnn-size-cost-structured-sparsity-accuracy"},"modified":"2025-06-10T10:03:24","modified_gmt":"2025-06-10T15:03:24","slug":"1-comment-on-maryland-u-google-introduce-lilnetx-simultaneously-optimizing-dnn-size-cost-structured-sparsity-accuracy","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/06\/1-comment-on-maryland-u-google-introduce-lilnetx-simultaneously-optimizing-dnn-size-cost-structured-sparsity-accuracy","title":{"rendered":"1 comment on \u201cMaryland U &amp; Google Introduce LilNetX: Simultaneously Optimizing DNN Size, Cost, Structured Sparsity &amp; Accuracy\u201d"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/1-comment-on-maryland-u-google-introduce-lilnetx-simultaneously-optimizing-dnn-size-cost-structured-sparsity-accuracy2.jpg\"><\/a><\/p>\n<p>The current conventional wisdom on deep neural networks (DNNs) is that, in most cases, simply scaling up a model\u2019s parameters and adopting computationally intensive architectures will result in large performance improvements. Although this scaling strategy has proven successful in research labs, real-world industrial deployments introduce a number of complications, as developers often need to repeatedly train a DNN, transmit it to different devices, and ensure it can perform under various hardware constraints with minimal accuracy loss.<\/p>\n<p>The research community has thus become increasingly interested in reducing such models\u2019 storage size on devices while also improving their run-time. Explorations in this area have tended to follow one of two avenues: reducing model size via compression techniques, or using model pruning to reduce computation burdens.<\/p>\n<p>In the new paper <em>LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification<\/em>, a team from the University of Maryland and Google Research proposes a way to \u201cbridge the gap\u201d between the two approaches with LilNetX, an end-to-end trainable technique for neural networks that jointly optimizes model parameters for accuracy, model size on the disk, and computation on any given task.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The current conventional wisdom on deep neural networks (DNNs) is that, in most cases, simply scaling up a model\u2019s parameters and adopting computationally intensive architectures will result in large performance improvements. Although this scaling strategy has proven successful in research labs, real-world industrial deployments introduce a number of complications, as developers often need to repeatedly [\u2026]<\/p>\n","protected":false},"author":732,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-215692","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/215692","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/732"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=215692"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/215692\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=215692"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=215692"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=215692"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}