{"id":"W2790087008","doi":"10.1145/3194554.3194626","title":"Comparative Study of Approximate Multipliers","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Adder; Multiplier (economics); Stochastic computing; Computer science; Circuit design; Booth's multiplication algorithm; Electronic circuit; Algorithm; Arithmetic; Mathematics; Embedded system; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001873856,0.0003863597,0.000707199,0.0002139137,0.00003468489,0.00003191307,0.0004830633,0.0002025986,0.0001765289],"category_scores_gemma":[0.000005028398,0.0003440771,0.00007522254,0.0001516047,0.00008163675,0.00008474293,0.000343861,0.0004413503,0.0001582037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009463853,"about_ca_system_score_gemma":0.00002852815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004923647,"about_ca_topic_score_gemma":0.00003425579,"domain_scores_codex":[0.9985516,0.00003640247,0.0004831601,0.0003534823,0.000290259,0.0002850792],"domain_scores_gemma":[0.9989088,0.00003932802,0.0001002709,0.0007668173,0.0001109512,0.00007390217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001218013,0.001558304,0.007929977,0.002207032,0.002842938,0.00001717313,0.0766704,0.8684144,0.004733113,0.0001525707,0.03436758,0.000984716],"study_design_scores_gemma":[0.00216708,0.000564036,0.006099319,0.0002223353,0.0001904456,0.000002167401,0.006287507,0.9288453,0.05353931,0.0002449553,0.000508689,0.001328879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602632,0.00007795314,0.01030134,0.000002151857,0.001224137,0.001311113,0.00001830306,0.0006176734,0.02618414],"genre_scores_gemma":[0.9939347,0.00001796,0.005540479,0.000004126194,0.000104578,0.0001312509,0.00001642172,0.00005530684,0.0001951943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07038289,"threshold_uncertainty_score":0.9999011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03345222231500609,"score_gpt":0.2694447678123757,"score_spread":0.2359925454973696,"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."}}