{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002200221,0.0001695581,0.0002111843,0.0006606877,0.0005875139,0.0000453908,0.0002321085,0.0001773035,0.00009162693],"category_scores_gemma":[0.000011735,0.000177582,0.00004270271,0.0004871661,0.00003752401,0.0002244246,0.00000270422,0.0004304083,0.00001543925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077957,"about_ca_system_score_gemma":0.00002059226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006749922,"about_ca_topic_score_gemma":0.00001126375,"domain_scores_codex":[0.9990898,0.00003425638,0.0002704968,0.0002477035,0.0001084736,0.0002493145],"domain_scores_gemma":[0.9993835,0.00009273062,0.00004354721,0.0004111173,0.00003460107,0.00003448099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008239251,0.00007286784,0.00003014978,0.0002451437,0.0001659924,0.000003911215,0.0006218561,0.7778224,0.02235842,0.002161821,0.001607611,0.1948274],"study_design_scores_gemma":[0.004776536,0.002738941,0.00008100345,0.0001927942,0.0002331883,0.0006792802,0.008654376,0.3105401,0.305222,0.02142711,0.34344,0.002014701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1164086,0.00128426,0.8730138,0.001812711,0.002471574,0.001712446,0.0001262645,0.001957923,0.001212414],"genre_scores_gemma":[0.9960545,0.0001189957,0.0006289373,0.00003162569,0.00003539804,0.002313545,0.00002619375,0.00003804556,0.0007527963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8796458,"threshold_uncertainty_score":0.7241583,"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."}}