{"id":4807,"date":"2025-11-25T03:38:49","date_gmt":"2025-11-25T03:38:49","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4807"},"modified":"2025-11-25T03:38:49","modified_gmt":"2025-11-25T03:38:49","slug":"ensemble-size-vs-latency-and-energy-on-cpu-gpu-for-rf-modulation-ensembles","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4807","title":{"rendered":"Ensemble Size vs Latency and Energy on CPU\/GPU for RF Modulation Ensembles"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"zO250Vdl0W\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4802\">Ensemble Size vs Latency and Energy on CPU\/GPU for RF Modulation Ensembles<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Ensemble Size vs Latency and Energy on CPU\/GPU for RF Modulation Ensembles&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4802&#038;embed=true#?secret=eX0GW0nmEq#?secret=zO250Vdl0W\" data-secret=\"zO250Vdl0W\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>Modern RF signal intelligence stacks increasingly rely<br>on ensembles of neural and classical models to stabilize<br>performance under changing channel conditions, hardware<br>front-ends, and signal mixes. Majority, weighted, and stacked<br>voting schemes can suppress idiosyncratic model failures, but<br>each additional model adds computation, memory traffic, and<br>host\u2013device synchronization overhead.<br>In resource-constrained deployments\u2014battery-powered field<br>nodes, embedded radios, or shared datacenter GPUs with strict<br>latency service-level agreements (SLAs)\u2014these costs manifest<br>as a hard cap on the number of signals that can be analyzed<br>per second. Understanding how latency and energy scale with<br>ensemble size is therefore critical for deciding whether \u201cjust<br>add another model\u201d is operationally viable.<br>This paper focuses on a concrete question: given a fixed<br>pool of RF modulation models, what is the latency\/energy cost<br>of increasing the ensemble size on CPU and GPU, and where<br>is the \u201cknee\u201d beyond which accuracy gains diminish?<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern RF signal intelligence stacks increasingly relyon ensembles of neural and classical models to stabilizeperformance under changing channel conditions, hardwarefront-ends, and signal mixes. Majority, weighted, and stackedvoting schemes can suppress idiosyncratic model failures, buteach additional model adds computation, memory traffic, andhost\u2013device synchronization overhead.In resource-constrained deployments\u2014battery-powered fieldnodes, embedded radios, or shared datacenter GPUs with strictlatency service-level&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4807\" rel=\"bookmark\"><span class=\"screen-reader-text\">Ensemble Size vs Latency and Energy on CPU\/GPU for RF Modulation Ensembles<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":4808,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[6,10],"tags":[],"class_list":["post-4807","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal-science","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4807","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4807"}],"version-history":[{"count":1,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4807\/revisions"}],"predecessor-version":[{"id":4809,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4807\/revisions\/4809"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/4808"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4807"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4807"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4807"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}