{"id":"W4211245589","doi":"10.32920/ryerson.14655717.v1","title":"Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scheduling (production processes); Design space exploration; High-level synthesis; Software; Genetic algorithm; Programming language; Distributed computing; Software engineering; Operating system; Embedded system; Field-programmable gate array; Engineering","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":[0.0008268125,0.0005632559,0.0004335036,0.0006067496,0.0003335846,0.000805624,0.001072357,0.0008076225,0.003983562],"category_scores_gemma":[0.001287449,0.0003368033,0.0006585339,0.0004204889,0.0004088144,0.0005351431,0.0005551639,0.0006927714,0.0007305876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005552208,"about_ca_system_score_gemma":0.001457113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002532112,"about_ca_topic_score_gemma":0.002733465,"domain_scores_codex":[0.9995523,0.0001230446,0.0000247049,0.00006863436,0.0001764682,0.00005484976],"domain_scores_gemma":[0.9995952,0.0001885066,0.00004394971,0.0000801922,0.00007314435,0.0000189879],"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":[0.0003040504,0.0002735802,0.002860014,0.000300653,0.000120375,0.0002513627,0.0003387689,0.51911,0.05657402,0.02292667,0.003889491,0.3930509],"study_design_scores_gemma":[0.00007442444,0.0001038542,0.0003973733,0.0000224522,0.00002770336,0.00008923228,0.00002485223,0.965157,0.02437836,0.003467915,0.006240549,0.00001629547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0150052,0.00005852287,0.9755102,0.00005448027,0.00002430287,0.00006956818,0.0000473072,0.006664503,0.002566013],"genre_scores_gemma":[0.1764399,0.00008712673,0.8204026,0.00006289853,0.000008016198,0.0001578483,0.0001822131,0.0004519865,0.002207489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003983562,"threshold_uncertainty_score":0.01332635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1174439703851813,"score_gpt":0.3368374681785186,"score_spread":0.2193934977933373,"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."}}