{"id":"W2130762555","doi":"10.1109/iccd.1994.331981","title":"An ILP solution for simultaneous scheduling, allocation, and binding in multiple block synthesis","year":2002,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Scheduling (production processes); Integer programming; Block (permutation group theory); Interdependence; Critical path method; Linear programming; Set (abstract data type); High-level synthesis; Mathematical optimization; Job shop scheduling; Processor scheduling; Theoretical computer science; Distributed computing; Parallel computing; Algorithm; Programming language; Mathematics; Embedded system; Engineering; Schedule; Field-programmable gate array","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001022907,0.00008930409,0.00009531578,0.0001051586,0.00004946038,0.00002741626,0.00005741496,0.00008253657,0.00002682021],"category_scores_gemma":[0.000135808,0.00009230194,0.00001604232,0.00007511936,0.000012459,0.0001214386,0.000005126165,0.0000499908,0.000007901636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003770495,"about_ca_system_score_gemma":0.00000158208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002227208,"about_ca_topic_score_gemma":0.0000601958,"domain_scores_codex":[0.9995181,0.000009322245,0.0001375725,0.000127572,0.00004523616,0.0001622209],"domain_scores_gemma":[0.9996229,0.0001971361,0.00001183409,0.0001044582,0.00002192102,0.00004173707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001523015,0.0002024006,0.005635056,0.0002653789,0.00004531688,0.00001001676,0.001259688,0.09916755,0.6307544,0.0005394301,0.001006798,0.2610987],"study_design_scores_gemma":[0.0001206036,0.00002527057,0.00004815208,0.00002107848,0.00000561191,0.000003677557,0.00003957461,0.9463567,0.0528687,0.00004019702,0.0003439883,0.0001264979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6356278,0.0002716394,0.3620906,0.00006427315,0.000048956,0.0004137614,0.000007301802,0.0009199714,0.0005556786],"genre_scores_gemma":[0.9458449,0.00009551401,0.05386361,0.00001256276,0.00002980865,0.00007061355,0.000002824483,0.00002146795,0.00005870597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8471891,"threshold_uncertainty_score":0.3763964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182484388445856,"score_gpt":0.2239941896528911,"score_spread":0.2021693457684325,"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."}}