{"id":"W4415702056","doi":"10.1145/3774327","title":"HiSpMM: High Performance High Bandwidth Sparse-Dense Matrix Multiplication on HBM-equipped FPGAs","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Scalability; Bottleneck; Field-programmable gate array; Workload; Speedup; Matrix multiplication; Design space exploration; Bandwidth (computing); Robustness (evolution)","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":[0.0003826394,0.0002697624,0.0003748111,0.001171481,0.0006102394,0.0001377005,0.0009631393,0.0004307358,0.00001431612],"category_scores_gemma":[0.00004469358,0.0002569046,0.00005273162,0.001202768,0.000116196,0.0002397941,0.00001866302,0.000473674,0.00007126979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009987954,"about_ca_system_score_gemma":0.00006707589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009772684,"about_ca_topic_score_gemma":0.000005494102,"domain_scores_codex":[0.9982236,0.0001046,0.0004716348,0.0006800519,0.0001609035,0.0003592224],"domain_scores_gemma":[0.9980385,0.0002089436,0.0001727047,0.001375687,0.0001446788,0.00005947473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002427776,0.000721773,0.0009282569,0.0004168369,0.0003443316,0.00002172501,0.0002500946,0.1747065,0.00387626,0.3416263,0.003259505,0.4736056],"study_design_scores_gemma":[0.003793543,0.001761287,0.002618948,0.001524471,0.0001170204,0.0002416635,0.0002284386,0.7738869,0.1724383,0.02699482,0.0148369,0.001557732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1169988,0.0004049328,0.8719212,0.004802347,0.0008997953,0.0006296202,0.00000867481,0.002338581,0.001996066],"genre_scores_gemma":[0.9801117,0.000494672,0.01339419,0.0001646903,0.00001682736,0.0002241222,0.000005255464,0.00001496751,0.005573611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8631129,"threshold_uncertainty_score":0.9999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360890834303751,"score_gpt":0.2504642010922213,"score_spread":0.2368552927491838,"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."}}