{"id":"W2073976813","doi":"10.1155/2013/130765","title":"An Impulse-C Hardware Accelerator for Packet Classification Based on Fine/Coarse Grain Optimization","year":2013,"lang":"en","type":"article","venue":"International Journal of Reconfigurable Computing","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Computer science; Hardware acceleration; Scalability; Speedup; Xeon; Network packet; Software; Computer hardware; Packet processing; Parallel computing; Computer engineering; Field-programmable gate array; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007365495,0.0001805959,0.0002187942,0.0003632072,0.0001826505,0.0007705645,0.001165846,0.00009119525,0.00007411446],"category_scores_gemma":[0.0002271063,0.0001661746,0.0001255426,0.0002561515,0.00002463756,0.001497569,0.00002128442,0.000202001,0.00001080495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466258,"about_ca_system_score_gemma":0.0002122714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007385642,"about_ca_topic_score_gemma":0.000001321872,"domain_scores_codex":[0.9981824,0.0001210535,0.0006722328,0.0003052925,0.0004762067,0.0002428128],"domain_scores_gemma":[0.9962183,0.0002682128,0.0009091813,0.000257177,0.002205421,0.0001416777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003680417,0.0001210189,0.0002913163,0.00001042059,0.00002972721,0.000004373899,0.0001389122,0.7658901,0.0004538067,0.0008758164,0.002677005,0.2294707],"study_design_scores_gemma":[0.0008876464,0.0002632704,0.0004313725,0.0001882949,0.000007537688,0.00003678819,0.00004649692,0.9951574,0.001421087,0.0008433202,0.0005394638,0.0001773146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007439155,0.00003151643,0.987054,0.002423107,0.001896565,0.0002198004,0.000004764971,0.00007796582,0.0008530559],"genre_scores_gemma":[0.6786622,0.000007654334,0.3199854,0.0006183914,0.0005989593,0.000008421821,0.00004278059,0.00001907997,0.00005711442],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6712231,"threshold_uncertainty_score":0.7430571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02434092692451534,"score_gpt":0.2871501366300257,"score_spread":0.2628092097055104,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}