{"id":"W4247622642","doi":"10.32920/ryerson.14653857.v1","title":"A rapid design space exploration approach for multi-objective optimization of DSP filter designs","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":"Design space exploration; Computer science; Digital signal processing; Process (computing); High-level synthesis; Design process; Resource (disambiguation); Filter (signal processing); Architecture; Embedded system; Computer architecture; Computer engineering; Field-programmable gate array; Computer hardware; Work in process; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001593748,0.0004601395,0.0007316734,0.0003984662,0.00009125594,0.0003810838,0.001278626,0.0005706358,0.00001448605],"category_scores_gemma":[0.0002633444,0.0004502037,0.0002940957,0.0004391289,0.00005316111,0.0009855756,0.0008021789,0.0003219192,0.000001271122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002095576,"about_ca_system_score_gemma":0.0005178371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006803832,"about_ca_topic_score_gemma":0.000003281178,"domain_scores_codex":[0.9962714,0.0009263814,0.0007745519,0.001247127,0.000440477,0.00034012],"domain_scores_gemma":[0.9961925,0.0003261528,0.0007494226,0.001504941,0.001137479,0.00008952018],"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":[0.00005133979,0.0005123676,0.000006647148,0.0008764198,0.0002251347,0.000004591078,0.008864383,0.9686701,0.006195632,0.007480082,0.00299487,0.004118416],"study_design_scores_gemma":[0.0003116489,0.0001696402,0.000002192484,0.0001524352,0.00002841541,0.000005272432,0.0003315375,0.8270332,0.1706796,0.0008923131,0.000008415973,0.0003852855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000005899993,0.0002903877,0.9932614,0.00007460076,0.000297259,0.004568047,0.00001029631,0.0006642438,0.0008278895],"genre_scores_gemma":[0.01987954,0.00008346453,0.97686,0.00005359025,0.00007488784,0.002486897,0.0001226436,0.00005553079,0.0003834201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.164484,"threshold_uncertainty_score":0.999795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1930801039678149,"score_gpt":0.3163942160850191,"score_spread":0.1233141121172042,"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."}}