{"id":"W3214953945","doi":"10.1109/cvprw56347.2022.00310","title":"MAPLE: Microprocessor A Priori for Latency Estimation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Latency (audio); Microprocessor; Artificial neural network; Deep learning; Field-programmable gate array; A priori and a posteriori; Efficient energy use; Energy consumption; Hardware architecture; Maple; Computer hardware; Computer engineering; Artificial intelligence; Embedded system; Software; 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":[0.0003807287,0.001488569,0.0004644795,0.0008683736,0.0003612458,0.0007035609,0.001134463,0.0005159412,0.004113573],"category_scores_gemma":[0.002913392,0.0004608742,0.0005089486,0.0005134813,0.000257218,0.00131574,0.0008751883,0.001103971,0.001484429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005581718,"about_ca_system_score_gemma":0.001251728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001717923,"about_ca_topic_score_gemma":0.004305539,"domain_scores_codex":[0.9995669,0.00005775786,0.00002543913,0.00009768243,0.0001987045,0.00005360499],"domain_scores_gemma":[0.9991869,0.0002616718,0.000149194,0.0001932695,0.0001714158,0.0000375098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005071808,0.0001733703,0.00773094,0.000350036,0.0001012318,0.0002164002,0.0001141682,0.2106452,0.05992342,0.008196489,0.01637124,0.6956702],"study_design_scores_gemma":[0.00002011332,0.0001800863,0.001676413,0.00003161489,0.00002027442,0.0001360579,0.00003223543,0.944711,0.04184096,0.005152111,0.00616427,0.00003479284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0426592,0.0008267904,0.9381925,0.0002443047,0.0001062319,0.0001127879,0.0007025787,0.01396403,0.003191574],"genre_scores_gemma":[0.602887,0.0004704784,0.3874699,0.000291684,0.00008565752,0.000468694,0.001880754,0.001055001,0.005390824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004113573,"threshold_uncertainty_score":0.01376134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04150276098953664,"score_gpt":0.294991536690126,"score_spread":0.2534887757005893,"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."}}