{"id":"W2946063265","doi":"10.23919/date.2019.8715183","title":"Thermal-Aware Design and Flow for FPGA Performance Improvement","year":2019,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Defense Advanced Research Projects Agency; University of Toronto","keywords":"Field-programmable gate array; Computer science; Overhead (engineering); Margin (machine learning); Design flow; Embedded system; Range (aeronautics); Computer engineering; Engineering; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003074431,0.000733713,0.0003426202,0.0004765209,0.0003258127,0.0005668943,0.0009331941,0.0003184677,0.002659211],"category_scores_gemma":[0.0004800514,0.0002869857,0.0003231123,0.0005561769,0.0002933127,0.0007702044,0.0002947609,0.0004425487,0.0005412463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007823849,"about_ca_system_score_gemma":0.000988152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001609684,"about_ca_topic_score_gemma":0.004409054,"domain_scores_codex":[0.9997423,0.00004206076,0.0000159784,0.00005699837,0.00008127459,0.00006134436],"domain_scores_gemma":[0.9997875,0.00005287626,0.00004721256,0.00004926946,0.00005279116,0.00001035559],"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":[0.000358617,0.0002025749,0.001938101,0.0004635817,0.0000700356,0.0001951424,0.0002069741,0.3471269,0.2848594,0.01695088,0.005283515,0.3423443],"study_design_scores_gemma":[0.00007105166,0.000517579,0.001806342,0.0000595353,0.0001057233,0.0002131301,0.00007578717,0.8520495,0.1194095,0.01034328,0.01529609,0.000052411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1133576,0.002747033,0.8646692,0.0004289637,0.0001056392,0.0001733768,0.0001822273,0.00403181,0.01430411],"genre_scores_gemma":[0.619965,0.001101622,0.3737383,0.0002187056,0.00007147644,0.0001745192,0.0002075075,0.000261879,0.004260896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002659211,"threshold_uncertainty_score":0.008895934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007769199085183681,"score_gpt":0.1798552100590251,"score_spread":0.1720860109738414,"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."}}