{"id":"W4406428192","doi":"10.1145/3711926","title":"Maximizing Data and Hardware Reuse for HLS with Early-Stage Symbolic Partitioning","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Architecture and Code Optimization","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Reuse; High-level synthesis; Parallel computing; Computer architecture; Embedded system; Field-programmable gate array; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001642717,0.0001613493,0.0001575375,0.0002347523,0.00051888,0.0002749306,0.0007211983,0.00007170458,0.000003324907],"category_scores_gemma":[0.00006197295,0.0001442261,0.00002241401,0.0003670115,0.00005176146,0.0003267118,0.00008299253,0.00015695,2.830939e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001386217,"about_ca_system_score_gemma":0.00004718485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001317939,"about_ca_topic_score_gemma":0.00002996932,"domain_scores_codex":[0.9989533,0.00005236066,0.0001825804,0.000519659,0.0001073983,0.0001846756],"domain_scores_gemma":[0.9984722,0.0001753153,0.00006845254,0.001144183,0.00008097207,0.00005887222],"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":[0.00005698902,0.00003732432,0.00007795446,0.00004451794,0.00004167479,0.00000110723,0.0005823004,0.9365881,0.00001441115,0.001400529,0.00009951805,0.06105554],"study_design_scores_gemma":[0.0006637973,0.0001584192,0.0001692032,0.0001592803,0.00004402287,0.00001106372,0.00003187321,0.9919062,0.0007060241,0.002836192,0.003097559,0.0002163141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006383039,0.0001071009,0.9952142,0.002983575,0.00004980191,0.0003636634,0.00006119666,0.0003590806,0.000223055],"genre_scores_gemma":[0.08230866,0.0002238813,0.9164491,0.0004946145,0.00001291869,0.0000611926,0.00004992478,0.00001405501,0.0003856774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08167035,"threshold_uncertainty_score":0.5881368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02344639361031738,"score_gpt":0.2755962635566579,"score_spread":0.2521498699463405,"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."}}