{"id":"W4416725785","doi":"10.1109/mwscas53549.2025.11244432","title":"Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edge","year":2025,"lang":"","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; GlobalFoundries; Qualcomm","keywords":"Deep learning; Enhanced Data Rates for GSM Evolution; Domain (mathematical analysis); Nanopore sequencing; Mobile device; Genomics; Signal processing; DNA sequencing","routes":{"ca_aff":true,"ca_fund":true,"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","sts"],"consensus_categories":[],"category_scores_codex":[0.001380671,0.0003442755,0.0003248712,0.0001646702,0.001303426,0.0005526327,0.001580299,0.0001994327,0.00006133736],"category_scores_gemma":[0.0001052851,0.0002639394,0.0002294516,0.0005777439,0.0001624262,0.0001824918,0.0007532315,0.0002714603,0.00006364047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007713314,"about_ca_system_score_gemma":0.0008593235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002241808,"about_ca_topic_score_gemma":0.000006404361,"domain_scores_codex":[0.9975358,0.0002733289,0.000565393,0.0008409746,0.0002075672,0.0005769342],"domain_scores_gemma":[0.997124,0.001145561,0.0001859199,0.00117364,0.0002839466,0.00008692897],"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":[0.00006282958,0.00007894726,0.00001415643,0.00005082529,0.00006628461,0.000001866551,0.001110236,0.7474903,0.0009678252,0.04920704,0.1201969,0.08075275],"study_design_scores_gemma":[0.0003276495,0.0003585102,0.000006715216,0.00007534854,0.00002230428,0.000003782205,0.00004662001,0.8825133,0.08506938,0.0058394,0.02546282,0.0002741243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001033133,0.0008426305,0.9830765,0.003580093,0.000896988,0.001925495,0.000004905425,0.0004762439,0.008163995],"genre_scores_gemma":[0.3841577,0.0006195532,0.5093846,0.03106568,0.0003902962,0.0007790396,0.000009845683,0.00006198353,0.07353134],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4736919,"threshold_uncertainty_score":0.9999967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.039555739604592,"score_gpt":0.298000484499992,"score_spread":0.2584447448954,"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."}}