{"id":"W2541839172","doi":"10.1145/3123939.3123982","title":"Bit-pragmatic deep neural network computing","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Ferroelectric and Negative Capacitance Devices","field":"Engineering","cited_by":229,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Massively parallel; Multiplication (music); Convolutional neural network; Parallel computing; Efficient energy use; Computation; Artificial neural network; Inference; Inefficiency; Computer engineering; Theoretical computer science; Algorithm; Artificial intelligence","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":[0.0005029318,0.00042762,0.0003492284,0.0002754387,0.0003841734,0.001064663,0.001613828,0.0006655313,0.006677668],"category_scores_gemma":[0.002007036,0.0002658369,0.0002380162,0.0005377511,0.000892562,0.002208983,0.001091514,0.001226975,0.001555839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006117938,"about_ca_system_score_gemma":0.0007013876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006154698,"about_ca_topic_score_gemma":0.001780074,"domain_scores_codex":[0.9995872,0.00008896813,0.00002952098,0.00006595699,0.0001832572,0.00004496881],"domain_scores_gemma":[0.9994745,0.0001476336,0.00003360762,0.0002029793,0.000118612,0.00002262318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005700938,0.0001268081,0.001278423,0.0006519524,0.00006733596,0.0002680574,0.0001922142,0.08640565,0.05755933,0.573469,0.02386904,0.255542],"study_design_scores_gemma":[0.00008073841,0.0001405408,0.0003339409,0.00006458204,0.00003260578,0.0002314888,0.00005142334,0.5792202,0.0281317,0.3522812,0.03940088,0.00003073707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05817999,0.002014365,0.891876,0.002250963,0.0003276936,0.0001189141,0.0004000382,0.002586427,0.04224567],"genre_scores_gemma":[0.6128118,0.001121889,0.3691557,0.0009983133,0.0001340074,0.0002137915,0.000544003,0.0002361378,0.01478426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006677668,"threshold_uncertainty_score":0.02233899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705274082514729,"score_gpt":0.2423718864989099,"score_spread":0.2253191456737627,"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."}}