{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79922"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79922","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"A 39fJ Analog Artificial Neural Network Classifier Circuit for Breast Cancer Classification In 65nm CMOS","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Hua, Ruobing"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sanyal, Arindam","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-30T15:11:07Z","date_published":"2019-07-30T15:11:07Z","updated_at":"2026-07-27T19:05:21Z","subjects":["electrical engineering","bioinformatics"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79922","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sanyal, Arindam","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Hua, Ruobing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-07-30T15:11:07Z","2019","2019-05-13 11:11:17"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["electrical engineering","bioinformatics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79922"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","An analog artificial neural network (ANN) classifier using a common-source amplifier based nonlinear activation function is presented in this work. A shallow ANN is designed in 65nm CMOS to perform binary classification on breast cancer dataset and identify each patient data as either benign or malignant. Use of common-source amplifier structure simplifies the ANN and results in only 39fJ/classification at 0.8V power supply and core area of only 240um2. The classifier is trained using MATLAB and validated using Spectre simulations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A 39fJ Analog Artificial Neural Network Classifier Circuit for Breast Cancer Classification In 65nm CMOS"]}]}],"canonical_facts":{"dc:contributor":["Sanyal, Arindam","Electrical Engineering"],"dc:creator":["Hua, Ruobing"],"dc:date":["2019-07-30T15:11:07Z","2019","2019-05-13 11:11:17"],"dc:description":["M.S.","An analog artificial neural network (ANN) classifier using a common-source amplifier based nonlinear activation function is presented in this work. A shallow ANN is designed in 65nm CMOS to perform binary classification on breast cancer dataset and identify each patient data as either benign or malignant. Use of common-source amplifier structure simplifies the ANN and results in only 39fJ/classification at 0.8V power supply and core area of only 240um2. The classifier is trained using MATLAB and validated using Spectre simulations."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79922"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["electrical engineering","bioinformatics"],"dc:title":["A 39fJ Analog Artificial Neural Network Classifier Circuit for Breast Cancer Classification In 65nm CMOS"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:21Z"}