{"id":"W4384947937","doi":"10.1109/iscas46773.2023.10181383","title":"FinFET 6T-SRAM Compute-in-Memory Targeting Low Power Neural Networks Operations","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Static random-access memory; Bottleneck; Artificial neural network; Computer science; Latency (audio); Power (physics); Parallel computing; Computation; Key (lock); Computer engineering; Computer hardware; Artificial intelligence; Embedded system; Algorithm","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":[0.00007683226,0.0003591998,0.0002715077,0.0002170886,0.0002933882,0.000581124,0.001684774,0.000565722,0.009618074],"category_scores_gemma":[0.0002541159,0.0001175302,0.0002130256,0.0003692491,0.0001288315,0.000981271,0.0002495157,0.0002628496,0.002157448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004768253,"about_ca_system_score_gemma":0.0003236762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009378756,"about_ca_topic_score_gemma":0.002937187,"domain_scores_codex":[0.9999282,0.000004644191,0.00000468657,0.00002134803,0.00002328171,0.00001790121],"domain_scores_gemma":[0.999898,0.00001758087,0.00002147326,0.00001332728,0.00004016196,0.000009519428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008503758,0.0003789658,0.004355317,0.002025028,0.0001945333,0.002691696,0.0002715358,0.03233463,0.6664942,0.04007225,0.06156769,0.1887638],"study_design_scores_gemma":[0.0001104517,0.001941874,0.003178963,0.000311106,0.0001803358,0.002786188,0.0002163388,0.2322983,0.6563984,0.00928107,0.09321803,0.00007898668],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6153956,0.008788877,0.2115161,0.002778903,0.00210626,0.0002250956,0.0037608,0.008037293,0.1473911],"genre_scores_gemma":[0.936439,0.001057482,0.03365209,0.0004102357,0.0001189925,0.00008924396,0.000743671,0.0001177412,0.02737159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009618074,"threshold_uncertainty_score":0.03217566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017093917247955,"score_gpt":0.2328733541252956,"score_spread":0.222702414952816,"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."}}