{"id":"W3089057106","doi":"10.1109/tcsii.2020.3026642","title":"AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Memristor; Convolutional neural network; Artificial neural network; Artificial intelligence; Scheme (mathematics); Pattern recognition (psychology); Algorithm; Electronic engineering; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006294193,0.0004695721,0.0002652968,0.000281423,0.0002860809,0.0004331892,0.001350839,0.0003915965,0.001715936],"category_scores_gemma":[0.001077163,0.0002294474,0.0001820527,0.0001513702,0.000258684,0.000597042,0.0006905372,0.0008731905,0.0002066925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004247862,"about_ca_system_score_gemma":0.0006078077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001609197,"about_ca_topic_score_gemma":0.002991096,"domain_scores_codex":[0.9998617,0.00002696733,0.000008710959,0.00002934397,0.00005412151,0.00001909664],"domain_scores_gemma":[0.9997712,0.00007701201,0.00003774736,0.00003734482,0.00006074116,0.00001587441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004290234,0.0001581264,0.002484794,0.0002151898,0.0001222823,0.0001875255,0.0002244401,0.3449003,0.1326065,0.01878677,0.003932604,0.4959525],"study_design_scores_gemma":[0.00000931016,0.00004848062,0.0001952022,0.000007178448,0.000009503706,0.00003630266,0.000008119692,0.9759215,0.02111015,0.001515206,0.001132389,0.000006685602],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02741971,0.0003081723,0.9694724,0.000116841,0.00007294896,0.00004000061,0.0000305278,0.001538048,0.001001368],"genre_scores_gemma":[0.7205495,0.0001481263,0.2749327,0.0001923318,0.00004547049,0.00009551064,0.00008630462,0.0001544715,0.00379562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001715936,"threshold_uncertainty_score":0.005740345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0377382900993719,"score_gpt":0.2473695638933039,"score_spread":0.209631273793932,"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."}}