{"id":"W4413088524","doi":"10.1109/ssp64130.2025.11073129","title":"Coding for Computation: Efficient Compression of Neural Networks for Reconfigurable Hardware","year":2025,"lang":"en","type":"article","venue":"","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Data compression; Computation; Coding (social sciences); Artificial neural network; Computer hardware; Computer architecture; Embedded system; Artificial intelligence; Algorithm; Mathematics","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":[0.0002087716,0.0004169875,0.0002678286,0.0004314914,0.0002119791,0.0004687586,0.0008533111,0.0003457222,0.002807816],"category_scores_gemma":[0.001108542,0.000127443,0.0001819465,0.00064685,0.0003476014,0.0008356686,0.0004978176,0.000523876,0.0005818019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003771812,"about_ca_system_score_gemma":0.000498101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001333578,"about_ca_topic_score_gemma":0.002452883,"domain_scores_codex":[0.999797,0.0000220379,0.00001561396,0.00002717754,0.0001138963,0.00002419218],"domain_scores_gemma":[0.9995987,0.0001285837,0.00003911517,0.0001391213,0.00008113839,0.00001337871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004054318,0.00009421848,0.0007693717,0.0002060259,0.00003959446,0.0002906698,0.0001074292,0.0994781,0.11368,0.03747432,0.009204434,0.7382505],"study_design_scores_gemma":[0.00004792578,0.0001868159,0.0006781258,0.00005127994,0.0000239833,0.0003110681,0.00004000807,0.8581554,0.1090744,0.01710356,0.01430401,0.00002344136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06702923,0.001207925,0.9199309,0.0003753212,0.0002194388,0.0001003913,0.0002460435,0.0029124,0.007978356],"genre_scores_gemma":[0.6122976,0.0006839788,0.3796213,0.000296738,0.0001265582,0.0001830519,0.000606385,0.0002172363,0.005967209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002807816,"threshold_uncertainty_score":0.009393096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0423625708863681,"score_gpt":0.314070213549232,"score_spread":0.271707642662864,"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."}}