{"id":4040,"date":"2025-10-17T23:57:52","date_gmt":"2025-10-17T23:57:52","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4040"},"modified":"2025-11-03T19:49:04","modified_gmt":"2025-11-03T19:49:04","slug":"flash-attention-mhla-for-rf-spectrumcompression-spectrumencoder-with-token-dropout-and-ropeablations","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4040","title":{"rendered":"Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations"},"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=\"bP84vGX4Q5\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4036\">Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4036&#038;embed=true#?secret=BrZfT7NbEt#?secret=bP84vGX4Q5\" data-secret=\"bP84vGX4Q5\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>RF monitoring stacks must compress and interpret highrate spectra under tight latency and power budgets. We target<br>the common case where the front-end produces windowed<br>FFT power spectra (magnitude-only) and the back-end must<br>(1) compress to a compact token sequence for downstream<br>classifiers, and (2) preserve class-relevant detail. We explore<br>multi-head linear attention (MHLA) with FlashAttention-style<br>backends and token-dropout as a simple, hardware-friendly<br>compressor.<br>II. BACKGROUND<br>FlashAttention &amp; Linear Attention. FlashAttention variants<br>reduce memory traffic for attention kernels; linear attention<br>further reduces quadratic costs. Rotary Positional Embeddings<br>(RoPE). RoPE injects relative position via complex rotations,<br>often improving extrapolation. Token-Dropout. We drop a<br>proportion r of lowest-energy bins (or a learned saliency proxy)<br>prior to attention, trading fidelity for speed and energy.<br>III. METHOD<br>A. SpectrumEncoder<br>Given an N-bin power spectrum x \u2208 R<br>N , we form<br>tokens by striding and optional pooling. We then apply tokendropout with rate r (by energy or entropy score), followed by<br>MHLA with a pluggable backend (Flash, grouped, or baseline).<br>Positional encoding uses RoPE, which we ablate by toggling<br>(none\/static\/dynamic-\u03b8).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>RF monitoring stacks must compress and interpret highrate spectra under tight latency and power budgets. We targetthe common case where the front-end produces windowedFFT power spectra (magnitude-only) and the back-end must(1) compress to a compact token sequence for downstreamclassifiers, and (2) preserve class-relevant detail. We exploremulti-head linear attention (MHLA) with FlashAttention-stylebackends and token-dropout as a&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4040\" rel=\"bookmark\"><span class=\"screen-reader-text\">Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":3380,"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-4040","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\/4040","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=4040"}],"version-history":[{"count":1,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4040\/revisions"}],"predecessor-version":[{"id":4041,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4040\/revisions\/4041"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/3380"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4040"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}