{"id":"W2162851503","doi":"10.1109/tvlsi.2008.2000731","title":"Low-Power Mixed-Signal CVNS-Based 64-Bit Adder for Media Signal Processing","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Adder; CMOS; Computer science; 16-bit; Electronic engineering; Serial binary adder; Carry-save adder; Dissipation; 4-bit; 8-bit; Signal processing; Arithmetic; Computer hardware; Digital signal processing; Mathematics; Engineering; Physics","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":[0.000153263,0.0003292462,0.0001877314,0.0004608775,0.0003434251,0.0005529306,0.0007353553,0.0002725766,0.003482711],"category_scores_gemma":[0.00031453,0.0001908319,0.000200875,0.0004017147,0.0001778294,0.0007122295,0.0002225751,0.0002355294,0.0008197813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003904784,"about_ca_system_score_gemma":0.0004608989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000476853,"about_ca_topic_score_gemma":0.002206044,"domain_scores_codex":[0.9998871,0.0000174729,0.0000102871,0.00002154876,0.00005152649,0.00001209101],"domain_scores_gemma":[0.9998347,0.00004694163,0.00002358839,0.00001864024,0.00006522168,0.00001090425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007857914,0.00008165509,0.001112208,0.000635559,0.000108132,0.0005223464,0.0001171261,0.01471033,0.5616024,0.03114405,0.004796635,0.3843838],"study_design_scores_gemma":[0.0001448221,0.001305118,0.002355517,0.000102265,0.0003010184,0.002023769,0.00008407232,0.2024351,0.671313,0.00820027,0.1116366,0.00009838634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2487737,0.005319099,0.7146344,0.0006302408,0.0008478358,0.0001790878,0.0004101993,0.002866542,0.02633895],"genre_scores_gemma":[0.6765064,0.0009929154,0.3096896,0.0002700279,0.0001185876,0.00008580294,0.0004145376,0.00007030005,0.01185178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003482711,"threshold_uncertainty_score":0.0116508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325311789925348,"score_gpt":0.2122322039523758,"score_spread":0.1989790860531223,"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."}}