{"id":389,"date":"2026-07-18T09:42:43","date_gmt":"2026-07-18T09:42:43","guid":{"rendered":"https:\/\/scalab.dimes.unical.it\/orsino\/?p=389"},"modified":"2026-07-18T09:42:43","modified_gmt":"2026-07-18T09:42:43","slug":"%f0%9f%93%a2-rise-dl-2026-workshop-is-out","status":"publish","type":"post","link":"https:\/\/scalab.dimes.unical.it\/orsino\/?p=389","title":{"rendered":"\ud83d\udce2 RISE-DL 2026 Workshop is out!"},"content":{"rendered":"<p>We&#8217;re excited to announce <strong>RISE-DL 2026 <\/strong>(Research and Innovation in Scalable and Efficient Deep Learning), co-located with the 16th International Conference on the Internet of Things<strong> (IoT 2026)<\/strong> in <strong>Newcastle upon Tyne, United Kingdom<\/strong>, on <strong>November 17, 2026<\/strong>.<\/p>\n<h2>Why RISE-DL?<\/h2>\n<p>As deep learning models continue to grow in scale and complexity, efficiency and sustainability need to become first-class objectives alongside raw performance. Today, progress on these fronts is scattered across separate communities\u2014algorithms, hardware, and systems\u2014which makes it harder to build end-to-end solutions that actually work in real-world deployments.<\/p>\n<p>RISE-DL brings these threads together in one forum, focused on efficient and scalable deep learning across the full spectrum of computing environments: from IoT and edge devices, to cloud and HPC infrastructure.<\/p>\n<h2>What We&#8217;re Looking For<\/h2>\n<p>We welcome original research, prior work, preliminary results, work-in-progress, and exploratory directions across the model lifecycle \u2014 design, training, inference, and deployment. Topics of interest include:<\/p>\n<ul>\n<li><strong>Training for long-term sustainability<\/strong> \u2014 quantization-aware training, pruning-aware training, knowledge distillation, green-aware neural architecture search<\/li>\n<li><strong>Scalable training of generative AI<\/strong> \u2014 KV-cache optimization for LLMs, memory-efficient attention, parameter-efficient fine-tuning<\/li>\n<li><strong>Fast inference and serving<\/strong> \u2014 speculative decoding, early-exit strategies, caching mechanisms<\/li>\n<li><strong>On-device, continual, and incremental learning<\/strong> \u2014 test-time adaptation, online and lifelong learning, edge personalization under resource constraints<\/li>\n<li><strong>Data-centric efficient deep learning<\/strong> \u2014 data pruning, compression, selection, dataset distillation<\/li>\n<li><strong>Benchmarking, metrics, and evaluation<\/strong> \u2014 energy consumption, latency, memory footprint, carbon emissions<\/li>\n<li><strong>Distributed learning across the edge\u2013cloud continuum<\/strong> \u2014 federated learning, split learning, gossip learning<\/li>\n<\/ul>\n<h2>Key Dates<\/h2>\n<table>\n<thead>\n<tr>\n<th>Milestone<\/th>\n<th>Deadline<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Paper submission<\/td>\n<td>August 31, 2026<\/td>\n<\/tr>\n<tr>\n<td>Author notification<\/td>\n<td>September 30, 2026<\/td>\n<\/tr>\n<tr>\n<td>Camera-ready submission<\/td>\n<td>October 2, 2026<\/td>\n<\/tr>\n<tr>\n<td>Workshop day<\/td>\n<td>November 17, 2026<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><em>All deadlines are 23:59 AoE (Anywhere on Earth).<\/em><\/p>\n<h2>Get Involved<\/h2>\n<p>Whether you&#8217;re working on efficient training, on-device intelligence, or scalable serving infrastructure, we&#8217;d love to see your work at RISE-DL 2026. Full details, including the organizing committee and program committee, are available on the <a href=\"https:\/\/rise-dl.github.io\/2026\/\">workshop website<\/a>.<\/p>\n<p>We look forward to your submissions and to seeing you in Newcastle this November!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We&#8217;re excited to announce RISE-DL 2026 (Research and Innovation in Scalable and Efficient Deep Learning), co-located with the 16th International[&#8230;]<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[14],"tags":[],"aioseo_notices":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=\/wp\/v2\/posts\/389"}],"collection":[{"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=389"}],"version-history":[{"count":2,"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=\/wp\/v2\/posts\/389\/revisions"}],"predecessor-version":[{"id":391,"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=\/wp\/v2\/posts\/389\/revisions\/391"}],"wp:attachment":[{"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=389"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=389"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scalab.dimes.unical.it\/orsino\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=389"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}