{"id":7359,"date":"2012-03-28T22:30:51","date_gmt":"2012-03-28T19:30:51","guid":{"rendered":"http:\/\/hgpu.org\/?p=7359"},"modified":"2012-03-28T22:30:51","modified_gmt":"2012-03-28T19:30:51","slug":"systematic-construction-verification-and-implementation-methodology-for-ldpc-codes","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7359","title":{"rendered":"Systematic construction, verification and implementation methodology for LDPC codes"},"content":{"rendered":"<p>In this article, a novel and systematic Low-density parity-check (LDPC) code construction, verification and implementation methodology is proposed. The methodology is composed by the simulated annealing based LDPC code constructor, the GPU based high-speed code selector, the ant colony optimization based pipeline scheduler and the FPGA-based hardware implementer. Compared to the traditional ways, this methodology enables us to construct both decoding performance-aware and hardware-efficiency-aware LDPC codes in a short time. Simulation results show that the generated codes have much less cycles (length 6 cycles eliminated) and memory conflicts (75% reduction on idle clocks), while having no BER performance loss compared to WiMAX codes. Additionally, the simulation speeds up by 490 times under float precision against CPU and a net throughput 24.5 Mbps is achieved. Finally, a net throughput 1.2 Gbps (bit-throughput 2.4 Gbps) multi-mode LDPC decoder is implemented on FPGA, with completely on-the-fly configurations and less than 0.2 dB BER performance loss.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this article, a novel and systematic Low-density parity-check (LDPC) code construction, verification and implementation methodology is proposed. The methodology is composed by the simulated annealing based LDPC code constructor, the GPU based high-speed code selector, the ant colony optimization based pipeline scheduler and the FPGA-based hardware implementer. Compared to the traditional ways, this methodology [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3,287],"tags":[1782,14,377,20,253,797,1800],"class_list":["post-7359","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-security","tag-computer-science","tag-cuda","tag-fpga","tag-nvidia","tag-nvidia-geforce-gtx-260","tag-parity-check-codes","tag-security"],"views":2193,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7359","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7359"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7359\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7359"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7359"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7359"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}