{"id":"W4396560422","doi":"10.1145/3663480","title":"DyRecMul: Fast and Low-Cost Approximate Multiplier for FPGAs using Dynamic Reconfiguration","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lookup table; Field-programmable gate array; Control reconfiguration; Multiplier (economics); Computation; Computer hardware; Digital signal processing; Parallel computing; Reconfigurable computing; Algorithm; Embedded system","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":[0.0002433767,0.0005817921,0.0003196619,0.0006161759,0.0002466607,0.0008293383,0.001038544,0.0002610016,0.006547129],"category_scores_gemma":[0.0006558891,0.0002100975,0.0002227606,0.0005353168,0.0002222158,0.0009557628,0.0005308403,0.0003926806,0.001987472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005121441,"about_ca_system_score_gemma":0.0005602607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006620138,"about_ca_topic_score_gemma":0.00163718,"domain_scores_codex":[0.9997602,0.00003215822,0.00001922699,0.0000469355,0.0001092625,0.00003216901],"domain_scores_gemma":[0.9998049,0.00004006305,0.00003236553,0.00005836761,0.00005338866,0.00001086183],"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":[0.0006844628,0.00007101534,0.001543289,0.0004161875,0.00007106561,0.0003197271,0.00009044854,0.03065753,0.104723,0.02692698,0.01689324,0.8176031],"study_design_scores_gemma":[0.0002773518,0.001637372,0.00234727,0.0001738681,0.0001009304,0.002333157,0.0001002172,0.5382229,0.268227,0.01370106,0.1727699,0.0001089655],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06761078,0.003022224,0.8949361,0.0002863181,0.0002809923,0.0001609731,0.0005610606,0.01134591,0.0217956],"genre_scores_gemma":[0.4814822,0.0009967565,0.4972947,0.0002883706,0.00009792278,0.0001869896,0.001421368,0.000480339,0.01775152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006547129,"threshold_uncertainty_score":0.02190232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01555394634372615,"score_gpt":0.2404817695593268,"score_spread":0.2249278232156007,"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."}}