{"id":"W2793377509","doi":"10.1145/3154425","title":"Enhancing FPGAs with Magnetic Tunnel Junction-Based Block RAMs","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Static random-access memory; Field-programmable gate array; Computer science; Scalability; Magnetoresistive random-access memory; Tunnel magnetoresistance; Block (permutation group theory); Embedded system; Transistor; Logic block; Computer hardware; Non-volatile memory; Electrical engineering; Random access memory; Materials science; Voltage; Nanotechnology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002828929,0.0002120939,0.0002596887,0.0006891112,0.0005784055,0.0001351315,0.0006323047,0.0002628466,0.00002629772],"category_scores_gemma":[0.00002605147,0.0001816477,0.00003665328,0.001031207,0.0002322446,0.0001604196,0.000007089131,0.0003009208,0.0000536005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003762093,"about_ca_system_score_gemma":0.00008051836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007158564,"about_ca_topic_score_gemma":0.00005419739,"domain_scores_codex":[0.9986229,0.00007142943,0.0003064551,0.000527121,0.0001465671,0.00032552],"domain_scores_gemma":[0.9985322,0.0001289136,0.0001153757,0.0009515733,0.0001978874,0.0000740571],"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":[0.0005668361,0.001709352,0.002246948,0.0006981528,0.0007126657,0.000151305,0.001876034,0.1676212,0.0250624,0.06494628,0.002980572,0.7314282],"study_design_scores_gemma":[0.003067962,0.007031569,0.000344919,0.0009762867,0.0001058414,0.001116853,0.0005891536,0.7221889,0.2382294,0.005958535,0.01875577,0.001634762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01055556,0.0003400584,0.9835541,0.001398552,0.00042654,0.0003115814,0.000001566517,0.001732272,0.001679779],"genre_scores_gemma":[0.9578871,0.00003996986,0.04042149,0.0001900022,0.00002876228,0.000105792,9.603297e-7,0.00001594603,0.001310042],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9473315,"threshold_uncertainty_score":0.7407377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211371524792153,"score_gpt":0.2226195462911986,"score_spread":0.210505831043277,"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."}}