{"id":"W2949216549","doi":"10.1109/fpt.2018.00026","title":"Tatum: Parallel Timing Analysis for Faster Design Cycles and Improved Optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Parallel computing; Kernel (algebra); Field-programmable gate array; Overhead (engineering); Correctness; Static timing analysis; Application-specific integrated circuit; Speedup; Multi-core processor; Compiler; Central processing unit; Embedded system; Computer hardware; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001049212,0.00009194206,0.0001246591,0.0001256425,0.00004862503,0.0000471185,0.00004884835,0.00006073733,0.00004645129],"category_scores_gemma":[0.000009017727,0.00008142949,0.00004016419,0.0001503131,0.0000231697,0.0001189828,0.000009984345,0.00002550039,0.000002080713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001123146,"about_ca_system_score_gemma":0.000002856158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006192775,"about_ca_topic_score_gemma":0.000006945138,"domain_scores_codex":[0.9995791,0.000008744036,0.0001189966,0.000120556,0.00003426459,0.0001383428],"domain_scores_gemma":[0.999768,0.0000395733,0.00001504463,0.0001030618,0.0000413353,0.00003293814],"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":[0.0001189328,0.00005753151,0.002180737,0.0001837471,0.001870294,0.000002041132,0.001586639,0.7786174,0.1277886,0.0009636949,0.01277203,0.07385828],"study_design_scores_gemma":[0.0001258806,0.00005912373,0.0001752808,0.000003247397,0.0001099538,5.78282e-7,0.00002272366,0.9783732,0.02071983,0.0002029158,0.0000882056,0.0001190403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001511181,0.00004973511,0.9966374,0.00001658029,0.00002528937,0.0002301917,0.000002346637,0.0004096054,0.001117673],"genre_scores_gemma":[0.4581176,0.00002483765,0.5415688,0.00003194506,0.00003812522,0.00003768105,0.00000640136,0.00001471563,0.0001598468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4566064,"threshold_uncertainty_score":0.3320598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844197518779713,"score_gpt":0.2460384257316878,"score_spread":0.2175964505438907,"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."}}