{"id":"W1546160363","doi":"10.5281/zenodo.38525","title":"Loop Optimization With Tradeoff Between Cycle Count And Code Size For Dsp Applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Very long instruction word; Computer science; Software pipelining; Compiler; Program optimization; Parallel computing; Digital signal processing; Code generation; Optimizing compiler; Code (set theory); Flexibility (engineering); Heuristic; Software; Instruction-level parallelism; Computer hardware; Programming language; Parallelism (grammar); Operating system","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":[0.000468818,0.0005239602,0.0003555522,0.0006512217,0.0003441809,0.0004447823,0.000496821,0.0003353886,0.003770454],"category_scores_gemma":[0.002221053,0.0002200864,0.0002335661,0.0006345807,0.000228494,0.0006887341,0.0003795474,0.0003896454,0.0003451308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003719731,"about_ca_system_score_gemma":0.0007812246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551931,"about_ca_topic_score_gemma":0.004995456,"domain_scores_codex":[0.9997233,0.00007696229,0.00001380737,0.00003575647,0.0001027114,0.00004746925],"domain_scores_gemma":[0.9990591,0.0005646091,0.00005770112,0.00007434479,0.0002118492,0.00003234228],"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":[0.002423746,0.0003815194,0.002208883,0.0002769493,0.00007323743,0.00009018191,0.0001703572,0.316105,0.1573365,0.007634322,0.004176441,0.5091228],"study_design_scores_gemma":[0.0001320044,0.0004670334,0.0008498103,0.00001844221,0.00005780743,0.00004436182,0.0000230116,0.9472598,0.04505977,0.004144506,0.001928618,0.00001483436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4659363,0.002964319,0.5134576,0.0005220247,0.0001002398,0.00008856365,0.0001241639,0.004165198,0.01264154],"genre_scores_gemma":[0.8076685,0.0003535175,0.185268,0.0001082907,0.00004770604,0.00006806803,0.0001420281,0.0005729857,0.005770929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003770454,"threshold_uncertainty_score":0.01261336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359594731624964,"score_gpt":0.2546215543915162,"score_spread":0.2410256070752666,"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."}}