{"id":"W4415382450","doi":"10.1145/3772082","title":"MAD-HiSpMV: Matrix Adaptive Design with Hybrid Row Distribution for Imbalanced SpMV Acceleration on FPGAs","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Speedup; Kernel (algebra); Multiplication (music); Benchmark (surveying); Matrix multiplication; Bottleneck; CUDA; Sparse matrix; Performance improvement","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":[0.0002141616,0.0007348112,0.0002564621,0.0004820075,0.000248206,0.0004852169,0.0008349367,0.0002367215,0.004232735],"category_scores_gemma":[0.0005582534,0.0002162065,0.0002843674,0.0003705508,0.0002290693,0.0005916057,0.0004461754,0.0004806822,0.00091824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004454218,"about_ca_system_score_gemma":0.0006710098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001168856,"about_ca_topic_score_gemma":0.002802399,"domain_scores_codex":[0.9997731,0.00004253949,0.00001348138,0.0000416191,0.00008345675,0.00004581369],"domain_scores_gemma":[0.9997742,0.00004893231,0.00004078517,0.00005721356,0.00005949167,0.00001942313],"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":[0.0005617872,0.0002461418,0.005270081,0.0004660561,0.0001087742,0.000531166,0.0002921401,0.222332,0.164291,0.0259074,0.02495472,0.5550387],"study_design_scores_gemma":[0.0001324122,0.0008686283,0.001312126,0.00005260747,0.00005413933,0.0003596183,0.00007762752,0.8756145,0.08188348,0.00824393,0.03135799,0.00004300401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1365099,0.001291416,0.8340383,0.0003469907,0.0001804545,0.0001499539,0.0002922492,0.01278279,0.01440793],"genre_scores_gemma":[0.6997675,0.0002321318,0.2937823,0.0003094903,0.00004071172,0.0001908195,0.000430306,0.0003418956,0.004904768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004232735,"threshold_uncertainty_score":0.01415992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02541017439712159,"score_gpt":0.2543381785465145,"score_spread":0.2289280041493929,"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."}}