{"id":"W3205197806","doi":"10.1109/fpl53798.2021.00070","title":"MAPLE: A Machine Learning based Aging-Aware FPGA Architecture Exploration Framework","year":2021,"lang":"en","type":"article","venue":"","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Government of Canada; Xilinx; Nvidia","keywords":"Field-programmable gate array; Computer science; Maple; Lookup table; Spice; Block (permutation group theory); Degradation (telecommunications); Computer architecture; Embedded system; Verilog; Artificial intelligence; Electronic engineering; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006302784,0.000133695,0.0001409103,0.00004557734,0.00005361392,0.0001105067,0.00006569266,0.00008581827,0.002437445],"category_scores_gemma":[0.0000494609,0.0001215545,0.00004109251,0.0001575363,0.000007521908,0.0001115791,0.00002120111,0.0002168529,0.0000625016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001806431,"about_ca_system_score_gemma":0.00001589013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002120567,"about_ca_topic_score_gemma":0.00004769085,"domain_scores_codex":[0.9993779,0.00003726194,0.0001425086,0.0001588286,0.0001134005,0.0001700766],"domain_scores_gemma":[0.9996569,0.00006853416,0.00001802585,0.0001701507,0.00003439152,0.00005201158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001594371,0.0000430947,0.001869274,0.0006463996,0.00009133945,0.00009987255,0.001652383,0.6217644,0.3594649,0.0007323329,0.001709033,0.011911],"study_design_scores_gemma":[0.0004943229,0.00003953469,0.0004534656,0.0002653238,0.00003276432,0.00002292372,0.0006451305,0.2411656,0.6884685,0.0028649,0.06487386,0.0006736685],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4364547,0.002051056,0.5532947,0.001078703,0.001556626,0.0001560308,0.00002631629,0.002012385,0.003369528],"genre_scores_gemma":[0.9935818,0.00003638874,0.005425751,0.0003374885,0.0002191701,0.00001242068,0.0001178496,0.00003830545,0.0002308075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5571271,"threshold_uncertainty_score":0.9984745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605384490017532,"score_gpt":0.2247115615233329,"score_spread":0.2086577166231575,"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."}}