{"id":"W2911319947","doi":"10.1109/rsp.2018.8631999","title":"Towards Trainable Synthesis for Optimized Circuit Deployment on FPGA","year":2018,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Field-programmable gate array; Computer science; Verilog; Software deployment; Embedded system; Leverage (statistics); Computer architecture; Hardware description language; Computer hardware; High-level synthesis; Routing (electronic design automation); Operating system","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":[0.0004758112,0.0009522847,0.0003838688,0.0006789778,0.0002993893,0.0007423713,0.001040172,0.0004109087,0.004715689],"category_scores_gemma":[0.001716045,0.0004208123,0.0004245721,0.0004700979,0.0003692441,0.0008553397,0.0005207175,0.0007531206,0.001023035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007837931,"about_ca_system_score_gemma":0.0009924618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857418,"about_ca_topic_score_gemma":0.003594282,"domain_scores_codex":[0.999466,0.0001288943,0.00004519921,0.00008484168,0.0001864378,0.00008865575],"domain_scores_gemma":[0.9991254,0.0003952465,0.0001055976,0.0001953387,0.0001564625,0.00002179764],"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":[0.0001926207,0.0002011806,0.001367598,0.0004459216,0.0000538502,0.0003997735,0.0002349863,0.4216711,0.2330323,0.02559061,0.006635752,0.3101743],"study_design_scores_gemma":[0.00007734435,0.000327853,0.0004827144,0.00005644012,0.00003900711,0.0001798329,0.00006228909,0.825672,0.1501155,0.008145426,0.01481388,0.00002769576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08183052,0.000269251,0.8962543,0.0002100089,0.00006227775,0.0001844784,0.0003392973,0.01002757,0.01082223],"genre_scores_gemma":[0.3698401,0.0002099589,0.6246696,0.0001319875,0.0000294413,0.0002487107,0.000587514,0.001058263,0.003224264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004715689,"threshold_uncertainty_score":0.0157755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03138668725022993,"score_gpt":0.2432699654459488,"score_spread":0.2118832781957189,"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."}}