{"id":"W4410299961","doi":"10.1016/j.sysarc.2025.103423","title":"GAROS: Genetic algorithm-aided row-skipping for shift and duplicate kernel mapping in processing-in-memory architectures","year":2025,"lang":"en","type":"article","venue":"Journal of Systems Architecture","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy; National Research Foundation of Korea; Samsung; Ministry of Education; Sungkyunkwan University","keywords":"Computer science; Kernel (algebra); Algorithm; Parallel computing; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004064006,0.000688198,0.000620472,0.0005611526,0.000522121,0.0004910483,0.001468875,0.0006624666,0.003726064],"category_scores_gemma":[0.001080878,0.0002760984,0.0004667778,0.000446289,0.0003812785,0.0005913041,0.0007055437,0.0007931843,0.0006575653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004843047,"about_ca_system_score_gemma":0.001384152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005360087,"about_ca_topic_score_gemma":0.009794364,"domain_scores_codex":[0.9997779,0.00005736697,0.000009120819,0.00003926111,0.00006722253,0.00004913774],"domain_scores_gemma":[0.9996369,0.000139985,0.00003223288,0.0000714524,0.00009439176,0.00002511686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006279236,0.0003591674,0.001686853,0.0001543551,0.0001286518,0.0002119633,0.0001665181,0.4473558,0.02730107,0.01105353,0.01051132,0.5004429],"study_design_scores_gemma":[0.00004068125,0.000100613,0.0001394611,0.000005065097,0.00001167803,0.00002683377,0.00001938632,0.9901515,0.006403481,0.001991076,0.001101386,0.000008830382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1486466,0.0004600851,0.8262313,0.0002512023,0.0002206542,0.000135932,0.000185645,0.01769303,0.006175558],"genre_scores_gemma":[0.4822168,0.00009135048,0.5119014,0.0001645608,0.00002717941,0.0001168439,0.0003279536,0.0006206171,0.004533343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005360087,"threshold_uncertainty_score":0.01246494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009903244853141379,"score_gpt":0.2517593208499379,"score_spread":0.2418560759967965,"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."}}