{"id":"W4387322640","doi":"10.48550/arxiv.2310.01022","title":"Subtractor-Based CNN Inference Accelerator","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Multiplication (music); Rounding; Adder; Sorting; Inference; Subtractor; Subtraction; Reduction (mathematics); Power (physics); Convolution (computer science); Preprocessor; Algorithm; Computer engineering; Parallel computing; Artificial intelligence; Electronic engineering; Deep learning; Arithmetic; Mathematics; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003316199,0.001002006,0.0004764766,0.0006710549,0.000426258,0.0009773481,0.003191245,0.0004755084,0.02177772],"category_scores_gemma":[0.0008462782,0.0004999912,0.0005338241,0.000786765,0.0002434897,0.001468304,0.0009149435,0.001135804,0.005514449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000969219,"about_ca_system_score_gemma":0.001193404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003476572,"about_ca_topic_score_gemma":0.008198996,"domain_scores_codex":[0.9996075,0.00002404509,0.00002718732,0.0001114049,0.0001644199,0.00006548542],"domain_scores_gemma":[0.9995673,0.00008124254,0.00003919699,0.00008911972,0.0001912813,0.00003185219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001700507,0.0003618536,0.003422938,0.0008550225,0.0002863983,0.0008696996,0.0002015217,0.03223885,0.2956219,0.02257257,0.06345976,0.578409],"study_design_scores_gemma":[0.0001764804,0.001021868,0.002130694,0.0001123611,0.0002766009,0.001227548,0.00009420382,0.5502433,0.3440918,0.007736582,0.09278803,0.0001005861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08838733,0.001994361,0.8466714,0.000694079,0.001244838,0.0003014554,0.001246882,0.02274399,0.03671565],"genre_scores_gemma":[0.5474616,0.0006750156,0.3915026,0.00130345,0.0002378181,0.0002377869,0.002404982,0.0006108977,0.05556577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02177772,"threshold_uncertainty_score":0.07285374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1816902550740911,"score_gpt":0.2126607136674553,"score_spread":0.0309704585933642,"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."}}