{"id":"W2483966489","doi":"10.1109/lca.2016.2597140","title":"Stripes: Bit-Serial Deep Neural Network Computing","year":2016,"lang":"en","type":"article","venue":"IEEE Computer Architecture Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computation; Acceleration; Artificial neural network; Overhead (engineering); Representation (politics); Set (abstract data type); Computer engineering; Energy (signal processing); Algorithm; Deep neural networks; State (computer science); Power (physics); Hardware acceleration; Efficient energy use; Artificial intelligence; Parallel computing; Computer hardware; Statistics; Mathematics; Electrical engineering","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":[0.0004400176,0.0006851635,0.0003536373,0.0005314967,0.0002715603,0.0007478305,0.001675236,0.0003080005,0.0135256],"category_scores_gemma":[0.001288465,0.0003242689,0.0003336215,0.001054197,0.0003640512,0.001528145,0.0008741911,0.0008322186,0.002295147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005966034,"about_ca_system_score_gemma":0.0008823785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002614708,"about_ca_topic_score_gemma":0.00551173,"domain_scores_codex":[0.9996973,0.00004223967,0.00002281952,0.0000543386,0.0001389559,0.00004445073],"domain_scores_gemma":[0.9994919,0.0001221581,0.0000467032,0.0001670523,0.0001330156,0.00003910715],"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":[0.002338076,0.0004591981,0.004561705,0.0006634295,0.0002458417,0.0003887208,0.0001625046,0.1098555,0.07303731,0.03527853,0.1174778,0.6555313],"study_design_scores_gemma":[0.0002566907,0.000580762,0.001443462,0.00006544848,0.00006203065,0.0002097771,0.00006512639,0.8662632,0.06349184,0.02580036,0.04170858,0.00005278836],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1948178,0.002693683,0.706332,0.001290436,0.001183809,0.0003200187,0.002456341,0.04659558,0.04431044],"genre_scores_gemma":[0.6754478,0.00108439,0.2892934,0.0007073107,0.0001438183,0.0003012562,0.004273794,0.0009369901,0.02781129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0135256,"threshold_uncertainty_score":0.04524767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009401555245308,"score_gpt":0.2262456919325462,"score_spread":0.2161516763800932,"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."}}