{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007810999,0.0003262456,0.0002879026,0.0001837712,0.00009865559,0.0000442474,0.0005184764,0.000262043,0.00005622308],"category_scores_gemma":[0.00004163611,0.0004090665,0.0001497121,0.0003753546,0.0000465398,0.0001509342,0.0003170801,0.0008629297,0.0002474448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001562346,"about_ca_system_score_gemma":0.00006710819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001192392,"about_ca_topic_score_gemma":0.0000149323,"domain_scores_codex":[0.9988324,0.00003543796,0.0001676168,0.0005522159,0.00005663984,0.0003556575],"domain_scores_gemma":[0.9989713,0.0002111074,0.0000749183,0.0005395954,0.00005988151,0.0001431669],"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":[0.00001268544,0.00001186045,0.0009745593,0.0001742246,0.00003106673,0.0002671205,0.00002517525,0.9962335,0.0009905004,0.0009539661,0.0001478343,0.0001775179],"study_design_scores_gemma":[0.0005280699,0.00003704832,0.003044353,0.000308619,0.00007992993,0.000001128989,0.0000554601,0.9716218,0.01432168,0.00788853,0.001079053,0.001034393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8856426,0.00003190032,0.1106337,0.00001484439,0.001079301,0.0001796766,0.0000267453,0.001637221,0.0007539958],"genre_scores_gemma":[0.998909,0.00008092758,0.0001603952,0.00004379088,0.0001408122,8.87647e-7,0.00003497615,0.00006238572,0.0005668243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1132664,"threshold_uncertainty_score":0.9998361,"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."}}