{"id":"W2000502252","doi":"10.1109/ainaw.2007.196","title":"Hardware-Software Cosynthesis of Multiprocessor Embedded Architectures","year":2007,"lang":"en","type":"article","venue":"","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Hypercube; Multiprocessing; Scheduling (production processes); Parallel computing; Embedded system; Software; Encoder; Memory footprint; Network topology; Computer architecture; Software architecture; Distributed computing; Topology (electrical circuits); Operating system; Engineering","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":[0.0003146602,0.0005260098,0.0002165242,0.0004639224,0.0002778087,0.0004090518,0.0003895892,0.0002704962,0.002956637],"category_scores_gemma":[0.0008714852,0.0001759753,0.0002231764,0.0002939535,0.0002953567,0.0006882997,0.0004242739,0.000320247,0.0005883261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002620304,"about_ca_system_score_gemma":0.000525229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004022959,"about_ca_topic_score_gemma":0.0007664794,"domain_scores_codex":[0.9997688,0.00005395127,0.00001609271,0.00004222852,0.00009321516,0.00002569311],"domain_scores_gemma":[0.9996529,0.0001383864,0.00003332222,0.00007870945,0.00008018138,0.00001651712],"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":[0.0004874759,0.0001253501,0.001381721,0.0004217845,0.00005200821,0.0005455101,0.0001509664,0.1148375,0.3678462,0.03255575,0.001075104,0.4805206],"study_design_scores_gemma":[0.0001163613,0.0009074436,0.002228177,0.000058424,0.00006984067,0.0007300365,0.0001500749,0.5419098,0.4083459,0.01873362,0.02671209,0.00003824347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1292232,0.0007900458,0.8573173,0.0001292865,0.00009924985,0.0001223885,0.00005611506,0.001693658,0.01056878],"genre_scores_gemma":[0.64372,0.0003492974,0.3508773,0.00005637792,0.00004050423,0.0001220909,0.0001947495,0.0002085437,0.004431196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002956637,"threshold_uncertainty_score":0.009890914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537069431437015,"score_gpt":0.2660168447278966,"score_spread":0.2506461504135264,"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."}}