{"id":"W4396817466","doi":"10.1109/tvlsi.2024.3394871","title":"A High Speed and Area Efficient Processor for Elliptic Curve Scalar Point Multiplication for GF(2<i> <sup>m</sup> </i>)","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Cryptography and Residue Arithmetic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Scalar multiplication; Elliptic curve point multiplication; Elliptic curve; Scalar (mathematics); Elliptic curve cryptography; Arithmetic; Elliptic Curve Digital Signature Algorithm; Multiplication (music); Point (geometry); Mathematics; Parallel computing; Computer science; Physics; Combinatorics; Pure mathematics; Geometry; Operating system; Public-key cryptography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001120573,0.0003042656,0.0002236169,0.0003582441,0.0002983172,0.0003071899,0.0005619285,0.000215507,0.005301822],"category_scores_gemma":[0.0002888936,0.0001045413,0.0001610245,0.0005825065,0.0001238319,0.0005143052,0.0002316442,0.0003353047,0.001625306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002043788,"about_ca_system_score_gemma":0.0003989105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001948663,"about_ca_topic_score_gemma":0.0004609564,"domain_scores_codex":[0.9999094,0.00001188343,0.000008459772,0.00002027193,0.000037831,0.00001214968],"domain_scores_gemma":[0.9998806,0.00001953898,0.00001717171,0.00001846491,0.00005617744,0.000008148199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006123508,0.0001513043,0.001625017,0.0004434127,0.00005207608,0.0005382663,0.0001838684,0.005898878,0.4478446,0.01133308,0.01363617,0.517681],"study_design_scores_gemma":[0.0002905163,0.003592072,0.005251582,0.0001161045,0.0001647545,0.005323631,0.0001903146,0.1761795,0.6707397,0.004137125,0.1339339,0.00008072921],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2218688,0.001488121,0.7497495,0.0003788397,0.0003277601,0.0003250551,0.0004007174,0.004817321,0.0206439],"genre_scores_gemma":[0.6330127,0.0005208623,0.349887,0.0001829378,0.0001311216,0.00014805,0.0007486714,0.00009675412,0.01527184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005301822,"threshold_uncertainty_score":0.01773638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268924643514694,"score_gpt":0.2392676144429189,"score_spread":0.226578368007772,"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."}}