{"id":"W4385237357","doi":"10.1109/tc.2023.3296899","title":"HPKA: A High-Performance CRYSTALS-Kyber Accelerator Exploring Efficient Pipelining","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Engineering and Physical Sciences Research Council; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Computer science; NIST; Post-quantum cryptography; Cryptography; Parallel computing; Field-programmable gate array; Embedded system; Public-key cryptography; Encryption; Algorithm; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002995556,0.0002229481,0.0001980129,0.0006277563,0.0006115414,0.0002532914,0.000604027,0.00004857029,0.00003361525],"category_scores_gemma":[0.000001547818,0.0002284206,0.0001265738,0.001941785,0.00004562531,0.0005162045,0.00001335182,0.0002563446,0.0001804576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006841527,"about_ca_system_score_gemma":0.00005466503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004112618,"about_ca_topic_score_gemma":0.00001056343,"domain_scores_codex":[0.9981291,0.00005699899,0.0003633072,0.0005316738,0.0004216157,0.000497257],"domain_scores_gemma":[0.9989693,0.0001824431,0.00007084306,0.0005581063,0.00007787076,0.0001414183],"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":[0.00002106887,0.0002578721,0.00004733634,0.000038683,0.0000883384,0.00003281528,0.005324085,0.7526916,0.001015938,0.001840553,0.001063413,0.2375783],"study_design_scores_gemma":[0.001036155,0.000209572,0.001784176,0.00008758215,0.0000167141,0.00001481971,0.0002476069,0.9814105,0.01304801,0.00008506652,0.001593759,0.0004660641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.440128,0.000003414162,0.557019,0.0002457123,0.001931802,0.0001304425,0.000007501916,0.0005041081,0.0000300573],"genre_scores_gemma":[0.9856839,0.00006073248,0.01373743,0.0002700077,0.00007222802,0.0001267985,0.000003980138,0.00002003393,0.00002486515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5455559,"threshold_uncertainty_score":0.931472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05966546207419866,"score_gpt":0.2679069968707073,"score_spread":0.2082415347965086,"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."}}