{"id":"W3161593038","doi":"10.1109/icfpt51103.2020.00057","title":"Towards Overlay-based Rapid In-Circuit Tuning of Deep Learning Designs","year":2020,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Field-programmable gate array; Computer science; Overlay; Debugging; Datapath; Computer architecture; Embedded system; Deep learning; Profiling (computer programming); Computer hardware; Artificial neural network; Artificial intelligence","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":[0.0008568125,0.0009765858,0.0003911837,0.0009367035,0.0002653993,0.001009686,0.001816698,0.0004854431,0.00416074],"category_scores_gemma":[0.004910925,0.0003583856,0.0002592672,0.0004040508,0.0004732443,0.001873214,0.001269105,0.0009913633,0.0006866863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008815544,"about_ca_system_score_gemma":0.0006986321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009667529,"about_ca_topic_score_gemma":0.001681007,"domain_scores_codex":[0.9988509,0.0002382192,0.00006426276,0.0001835649,0.000513456,0.0001495671],"domain_scores_gemma":[0.9975127,0.0008194632,0.0003374783,0.0008710575,0.0003737262,0.00008551124],"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":[0.0009136215,0.0003009347,0.00616719,0.0004355015,0.0001145723,0.0006676228,0.0006722808,0.1068145,0.3045591,0.02130327,0.008329781,0.5497216],"study_design_scores_gemma":[0.00008129294,0.0005143083,0.001488325,0.00005825706,0.00004870824,0.0004021839,0.0001171731,0.5727616,0.3861385,0.01626478,0.02207774,0.00004714715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1275097,0.0004461521,0.8407258,0.0002293578,0.0001308403,0.0001617326,0.0002362456,0.02183516,0.008725097],"genre_scores_gemma":[0.6992802,0.0001333924,0.2965718,0.000168656,0.0000309746,0.00009563642,0.0002075696,0.001038525,0.002473242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00416074,"threshold_uncertainty_score":0.01391906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06397177591804296,"score_gpt":0.2469605852393546,"score_spread":0.1829888093213117,"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."}}