{"id":"W2319292801","doi":"10.1109/tvlsi.2016.2535313","title":"Stochastic Circuit Design and Performance Evaluation of Vector Quantization for Different Error Measures","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Binary number; Computer science; Algorithm; Stochastic computing; Scalability; Vector quantization; Data compression; Norm (philosophy); Integrated circuit design; Mathematics; Computation; Arithmetic","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":[0.0006271875,0.000362724,0.000254856,0.0004685007,0.0002382498,0.0005664782,0.0008966919,0.0003971029,0.001878654],"category_scores_gemma":[0.002339049,0.000131441,0.0001946358,0.000578812,0.0002468532,0.0007500016,0.0002177787,0.0002741448,0.0002335931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100264,"about_ca_system_score_gemma":0.0006763045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001489506,"about_ca_topic_score_gemma":0.001906406,"domain_scores_codex":[0.9991162,0.000156777,0.00006884798,0.0001290485,0.0004493024,0.00007982994],"domain_scores_gemma":[0.9986969,0.0004000618,0.0002102498,0.0001480852,0.0005181548,0.00002660757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008060923,0.0002810456,0.00407574,0.0007664058,0.0001725039,0.0002643121,0.0002213357,0.2475433,0.4591897,0.03366287,0.004497047,0.2485197],"study_design_scores_gemma":[0.0001065796,0.001997047,0.002272437,0.00004930377,0.0000800502,0.0003595325,0.00006424968,0.6335067,0.3491601,0.002897646,0.009447763,0.00005859448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4975295,0.00138347,0.4865025,0.0006854127,0.0002430123,0.0002914582,0.0004337842,0.002205096,0.01072574],"genre_scores_gemma":[0.9187408,0.0002121788,0.07909997,0.00008815066,0.00002648119,0.00009553428,0.000171439,0.00005127459,0.001514094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001878654,"threshold_uncertainty_score":0.007274687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07401531795369443,"score_gpt":0.2907975500012648,"score_spread":0.2167822320475704,"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."}}