{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001788189,0.0004507345,0.0003754403,0.0001504057,0.0003801905,0.0002029035,0.002146612,0.0000948781,0.000007739402],"category_scores_gemma":[0.00001042601,0.0003264326,0.00020431,0.000765675,0.0001782995,0.0003732167,0.0005665246,0.0003976383,0.00008524889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007950114,"about_ca_system_score_gemma":0.00002039132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004069589,"about_ca_topic_score_gemma":0.00001140857,"domain_scores_codex":[0.9967431,0.0002177038,0.0004978196,0.00106137,0.0004097235,0.001070278],"domain_scores_gemma":[0.9974842,0.0007329269,0.0002358051,0.001233474,0.00005334855,0.0002602189],"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":[0.0000166819,0.00002359778,0.0002355599,0.00000741612,0.00003252955,0.00004749564,0.0002033964,0.4955583,0.01062096,0.001879409,0.007524256,0.4838504],"study_design_scores_gemma":[0.003769438,0.0004254811,0.007271097,0.0002654347,0.0000426849,0.0007222998,0.000002825875,0.9187754,0.004420516,0.01983995,0.04179258,0.002672336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06153609,0.00004560837,0.9143134,0.01974646,0.003063656,0.0003978105,0.000002873447,0.0008585252,0.000035522],"genre_scores_gemma":[0.5604418,0.000004006747,0.4169244,0.0165182,0.006020705,0.0000254775,0.000003570433,0.00004743829,0.00001436033],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4989057,"threshold_uncertainty_score":0.9999188,"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."}}