{"id":"W3109527654","doi":"10.1109/vlsitechnology18217.2020.9265100","title":"ExaNoDe: Combined Integration of Chiplets on Active Interposer with Bare Dice in a Multi-Chip-Module for Heterogeneous and Scalable High Performance Compute Nodes","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Horizon 2020 Framework Programme; Agence Nationale de la Recherche","keywords":"Scalability; Computer science; Modular design; Interposer; Dice; Node (physics); Computer architecture; Field-programmable gate array; Context (archaeology); Parallel computing; Supercomputer; Chip; Efficient energy use; Routing (electronic design automation); Embedded system; Computational science; Engineering; Materials science; Electrical engineering; Nanotechnology; Operating system; Layer (electronics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001865392,0.0003600719,0.00055333,0.0002467561,0.00007161914,0.0001436687,0.0007275128,0.0001808049,0.000001969238],"category_scores_gemma":[0.00003805773,0.0002959749,0.00006061694,0.0002318226,0.00007008792,0.0002010257,0.00080377,0.0003939144,0.000001084935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007842622,"about_ca_system_score_gemma":0.00009121076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001464152,"about_ca_topic_score_gemma":0.00005312417,"domain_scores_codex":[0.9981983,0.00009885498,0.0004299748,0.0008149483,0.0002200893,0.0002378345],"domain_scores_gemma":[0.9987913,0.0001453179,0.0002392576,0.0005121707,0.0002311722,0.00008079263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00202765,0.001396505,0.003438707,0.001635145,0.0003023983,0.0000202673,0.006776398,0.9012288,0.001800213,0.007284791,0.0003195099,0.07376961],"study_design_scores_gemma":[0.001103466,0.0008198731,0.005281671,0.0007125497,0.000009321698,0.000004698658,0.00001063467,0.943046,0.04823166,0.0004576073,0.000007449353,0.0003150786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2975509,0.00002209057,0.7010269,0.0002589308,0.00008988893,0.00071692,0.00001540633,0.0002374595,0.00008151026],"genre_scores_gemma":[0.6416054,0.00003074669,0.358019,0.0001708946,0.00001135492,0.00007535495,0.00004352441,0.00001706574,0.00002671877],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3440545,"threshold_uncertainty_score":0.9999492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03216909884349559,"score_gpt":0.2678285027556492,"score_spread":0.2356594039121536,"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."}}