{"id":"W4229451071","doi":"10.1145/3503465","title":"FPGA Architecture Exploration for DNN Acceleration","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Benchmark (surveying); Computer science; Field-programmable gate array; Computer architecture; Electronic circuit; Digital signal processing; Embedded system; Computer engineering; Routing (electronic design automation); Suite; Place and route; Computer hardware","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":[0.0004825272,0.000892392,0.0002280711,0.0007299873,0.0002202461,0.0005082863,0.0008202665,0.0003123861,0.005992588],"category_scores_gemma":[0.001430267,0.0001989053,0.0004307646,0.0005211279,0.0001832757,0.0006543239,0.000360317,0.0004143082,0.0005356113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009161347,"about_ca_system_score_gemma":0.0005759287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002968381,"about_ca_topic_score_gemma":0.006102488,"domain_scores_codex":[0.9997073,0.00008548877,0.00001607825,0.00004450776,0.00009625814,0.00005027356],"domain_scores_gemma":[0.999525,0.0002507663,0.00003474982,0.00006517853,0.0001069867,0.00001728778],"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":[0.0005331806,0.0002144473,0.005648127,0.0007139135,0.00008553496,0.000572552,0.0001401675,0.5563368,0.04472109,0.01864842,0.01449355,0.3578923],"study_design_scores_gemma":[0.00009162944,0.0005781587,0.001582353,0.00006443838,0.00004528469,0.0002609361,0.00009252273,0.9176019,0.04823016,0.009652921,0.02177517,0.00002448099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.634901,0.002812601,0.2915066,0.0007529951,0.0002915993,0.0003380528,0.002336637,0.01048631,0.0565741],"genre_scores_gemma":[0.8353199,0.000569635,0.1565805,0.0001618857,0.00001910947,0.0001387664,0.002154802,0.0005140737,0.004541399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005992588,"threshold_uncertainty_score":0.02004719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02245064147097768,"score_gpt":0.2190212713474694,"score_spread":0.1965706298764917,"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."}}