{"id":"W4210659427","doi":"10.1145/3501772","title":"A Genetic-algorithm-based Approach to the Design of DCT Hardware Accelerators","year":2022,"lang":"en","type":"article","venue":"ACM Journal on Emerging Technologies in Computing Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Discrete cosine transform; Computer science; Field-programmable gate array; Design space exploration; Computer engineering; Genetic algorithm; Abstraction; Pareto principle; Algorithm; Mathematical optimization; Computer hardware; Embedded system; Image (mathematics); Artificial intelligence; Mathematics; Machine learning","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":[0.0003073411,0.0005241258,0.000356741,0.0005648314,0.000281772,0.0005260903,0.0006782946,0.000537623,0.001356402],"category_scores_gemma":[0.0007812779,0.0002493086,0.0005079425,0.0005174335,0.0004906527,0.0002620644,0.0003104237,0.0005962322,0.0002432271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005790598,"about_ca_system_score_gemma":0.00104072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002757773,"about_ca_topic_score_gemma":0.003212228,"domain_scores_codex":[0.9997914,0.00004956051,0.000007871763,0.00003448247,0.0000933509,0.00002338003],"domain_scores_gemma":[0.999841,0.0000661512,0.00001938816,0.00001901827,0.00004666893,0.000007636635],"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":[0.00003080948,0.00003641748,0.0005426356,0.00007071438,0.00002610966,0.00005901729,0.00004538782,0.8992819,0.01582311,0.01770354,0.0005971533,0.0657833],"study_design_scores_gemma":[0.000009159318,0.00005579957,0.0001072781,0.000008400855,0.000009115834,0.00003347302,0.000009684654,0.9930773,0.002020244,0.003179923,0.001485076,0.000004591499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02060898,0.0002477508,0.9742007,0.0001082687,0.0000300867,0.00007058986,0.00003509507,0.0002401845,0.004458422],"genre_scores_gemma":[0.3337647,0.0003591816,0.662755,0.0001032198,0.00002276951,0.0002243671,0.0001159687,0.00005978658,0.002595015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002757773,"threshold_uncertainty_score":0.005483449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02608078517757905,"score_gpt":0.2370687272900442,"score_spread":0.2109879421124651,"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."}}