{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003480458,0.0003717145,0.0003409879,0.0002525356,0.0009227297,0.0003790469,0.0008128755,0.00009940943,0.0002329776],"category_scores_gemma":[0.00001495521,0.0003773674,0.0001205996,0.0006933871,0.00005481631,0.0004967637,0.000446316,0.0005145912,0.00008953417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008567145,"about_ca_system_score_gemma":0.00006913891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005939812,"about_ca_topic_score_gemma":0.000009756828,"domain_scores_codex":[0.9972354,0.0001849824,0.0005313364,0.001090481,0.0004847216,0.0004731081],"domain_scores_gemma":[0.9982557,0.0003860088,0.0003405104,0.000585819,0.000233459,0.000198534],"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":[0.00005842671,0.0002019075,0.00004064387,0.0000372469,0.00001774965,0.00001047414,0.0004475138,0.001436153,0.0008440872,0.001092195,0.007681173,0.9881324],"study_design_scores_gemma":[0.001344325,0.000841019,0.0006614749,0.0001318432,0.00002233073,0.00009626712,0.00005553321,0.9661367,0.000697332,0.0217468,0.00758282,0.0006835755],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02835694,0.00008350125,0.9649283,0.00369323,0.001266611,0.001075642,0.0001161224,0.0003303572,0.0001493298],"genre_scores_gemma":[0.892459,0.0002180988,0.1001858,0.004658258,0.0003372083,0.001217814,0.0004236484,0.00005584002,0.0004444039],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9874489,"threshold_uncertainty_score":0.9998678,"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."}}