{"id":4059,"date":"2025-10-18T14:59:59","date_gmt":"2025-10-18T14:59:59","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4059"},"modified":"2025-10-18T14:59:59","modified_gmt":"2025-10-18T14:59:59","slug":"flash-attention-mhla-for-rf-spectrumcompression-spectrumencoder-with-token-dropout-and-ropeablations-2","status":"publish","type":"page","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?page_id=4059","title":{"rendered":"Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations"},"content":{"rendered":"\n<p class=\"has-text-align-right wp-block-paragraph\">We present a lightweight SpectrumEncoder for<br>compressing FFT power spectra using multi-head linear attention<br>(MHLA) with FlashAttention backends and token-dropout. We<br>report compression\u2013accuracy trade-offs, latency profiles, and an<br>ablation on Rotary Positional Embeddings (RoPE). The method is<br>designed for real-time SIGINT pipelines where millisecond-level<br>latency and energy budgets matter, enabling up to 40% more<br>concurrent RF bands on the same hardware.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/gemini.google.com\/share\/c52be72b15d2\"><img loading=\"lazy\" decoding=\"async\" width=\"762\" height=\"711\" src=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-12.png\" alt=\"\" class=\"wp-image-4060\" srcset=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-12.png 762w, https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-12-300x280.png 300w\" sizes=\"auto, (max-width: 762px) 100vw, 762px\" \/><\/a><\/figure>\n<\/div>\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Flash-Attention-MHLA-for-RF-Spectrum-Compression-SpectrumEncoder-with-Token-Dropout-and-RoPE-Ablations.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Flash-Attention MHLA for RF Spectrum Compression SpectrumEncoder with Token-Dropout and RoPE Ablations.\"><\/object><a id=\"wp-block-file--media-6f24b1f8-ad4c-4d39-9513-b733b53d053f\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Flash-Attention-MHLA-for-RF-Spectrum-Compression-SpectrumEncoder-with-Token-Dropout-and-RoPE-Ablations.pdf\">Flash-Attention MHLA for RF Spectrum Compression SpectrumEncoder with Token-Dropout and RoPE Ablations<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Flash-Attention-MHLA-for-RF-Spectrum-Compression-SpectrumEncoder-with-Token-Dropout-and-RoPE-Ablations.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-6f24b1f8-ad4c-4d39-9513-b733b53d053f\">Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>We present a lightweight SpectrumEncoder forcompressing FFT power spectra using multi-head linear attention(MHLA) with FlashAttention backends and token-dropout. Wereport compression\u2013accuracy trade-offs, latency profiles, and anablation on Rotary Positional Embeddings (RoPE). The method isdesigned for real-time SIGINT pipelines where millisecond-levellatency and energy budgets matter, enabling up to 40% moreconcurrent RF bands on the same hardware.<\/p>\n","protected":false},"author":2,"featured_media":4060,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","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":""},"class_list":["post-4059","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4059","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4059"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4059\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/4060"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4059"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}