{"id":"W4292633129","doi":"10.1007/978-3-030-98347-5_6","title":"Majority Logic-Based Approximate Multipliers for Error-Tolerant Applications","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; University of Alberta","funders":"","keywords":"Multiplier (economics); Computer science; Electronic circuit; Algorithm; Artificial neural network; Arithmetic; Mathematics; Artificial intelligence; Engineering; Electrical engineering","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":[0.0001653763,0.0005771861,0.000303289,0.0003981852,0.0002841422,0.001038813,0.001084394,0.0004158397,0.01197946],"category_scores_gemma":[0.000512514,0.0002825589,0.000198095,0.0006639292,0.0002750063,0.00156844,0.0004801296,0.0008882814,0.003274108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004352105,"about_ca_system_score_gemma":0.0003060665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000268127,"about_ca_topic_score_gemma":0.001139592,"domain_scores_codex":[0.9998553,0.00001788309,0.00000741915,0.00002511133,0.00008194965,0.00001229532],"domain_scores_gemma":[0.9998614,0.0000421104,0.00001365952,0.00002553794,0.00005236527,0.000004880987],"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":[0.0002122573,0.00007026694,0.0001992102,0.0005744646,0.00006167719,0.000107875,0.0001059559,0.0246775,0.09865139,0.2285586,0.02322636,0.6235545],"study_design_scores_gemma":[0.00007056005,0.0006129876,0.0005049837,0.0002920615,0.0001384062,0.001232104,0.0001139346,0.2816924,0.1784019,0.174001,0.3628756,0.00006400837],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01622479,0.01482833,0.8756649,0.0006940803,0.0009752033,0.0001170308,0.000239889,0.001460889,0.08979489],"genre_scores_gemma":[0.2991007,0.01206555,0.4777694,0.0008871983,0.0005305102,0.000133321,0.0003938041,0.000441723,0.2086777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01197946,"threshold_uncertainty_score":0.0400753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02408923146320867,"score_gpt":0.2259186012384266,"score_spread":0.2018293697752179,"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."}}