{"id":"W4416429575","doi":"10.1109/iccad66269.2025.11241002","title":"Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Chinese Academy of Sciences","keywords":"Field-programmable gate array; Hummingbird; Limiting; Inference; Bandwidth (computing); Scheme (mathematics); Booster (rocketry); Unification","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":[0.0002424207,0.0008983882,0.0003202418,0.0003869394,0.0002189538,0.0005789031,0.00175502,0.0003590675,0.01825471],"category_scores_gemma":[0.0009958503,0.0003616385,0.0003670559,0.0003074407,0.0002546009,0.001208543,0.0006858174,0.0008394328,0.003705436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006652171,"about_ca_system_score_gemma":0.0008158766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004221581,"about_ca_topic_score_gemma":0.007399878,"domain_scores_codex":[0.9997938,0.00002533662,0.00001198818,0.00006004631,0.00006755709,0.00004113677],"domain_scores_gemma":[0.9997658,0.00008036921,0.00002488979,0.00004867768,0.00004856463,0.00003178447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003914556,0.0005141862,0.01151191,0.001168049,0.0004660964,0.001595096,0.0005642053,0.1232188,0.1441901,0.0192527,0.1701796,0.5234247],"study_design_scores_gemma":[0.0006493402,0.001229257,0.003109016,0.0001098661,0.0001386313,0.0005813752,0.0002262926,0.7916411,0.08851497,0.00549564,0.1081921,0.0001123937],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3014703,0.002991207,0.5156503,0.001145782,0.001069664,0.0005184483,0.003979808,0.1296475,0.04352701],"genre_scores_gemma":[0.7217478,0.000481612,0.2531221,0.0009251064,0.00006351688,0.0002856035,0.003782309,0.001886613,0.01770535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01825471,"threshold_uncertainty_score":0.06106812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02279458213259505,"score_gpt":0.3028497259613687,"score_spread":0.2800551438287737,"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."}}