{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/41637"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/41637","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"Fine Granularity is Critical for Intelligent Neural Network Pruning","abstract":"Neural network pruning is a popular approach to reducing the computational costs of training and/or deploying a network, and aims to do so while minimizing accuracy loss. Pruning methods that remove individual weights (fine granularity) yield better ratios of accuracy to parameter count, while methods that preserve some or all of a network’s structure (coarser granularity, e.g. pruning channels from a CNN) take better advantage of hardware and software optimized for dense matrix computations. We compare intelligent iterative pruning using several different criteria sampled from the literature against random pruning at initialization across multiple granularities on two different image classification architectures and tasks. We find that the advantage of intelligent pruning (with any criterion) over random pruning decreases dramatically as granularity becomes coarser. Our results suggest that, compared to coarse pruning, fine pruning combined with efficient implementation of the resulting networks is a more promising direction for improving accuracy-to-cost ratios.","abstract_html":"Neural network pruning is a popular approach to reducing the computational costs of training and/or deploying a network, and aims to do so while minimizing accuracy loss. Pruning methods that remove individual weights (fine granularity) yield better ratios of accuracy to parameter count, while methods that preserve some or all of a network’s structure (coarser granularity, e.g. pruning channels from a CNN) take better advantage of hardware and software optimized for dense matrix computations. We compare intelligent iterative pruning using several different criteria sampled from the literature against random pruning at initialization across multiple granularities on two different image classification architectures and tasks. We find that the advantage of intelligent pruning (with any criterion) over random pruning decreases dramatically as granularity becomes coarser. Our results suggest that, compared to coarse pruning, fine pruning combined with efficient implementation of the resulting networks is a more promising direction for improving accuracy-to-cost ratios.","abstract_has_math":false,"creators":["Heyman, Andrew Baldwin"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Zylberberg, Joel"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12-08","date_published":"2023-12-08","updated_at":"2026-07-24T06:34:03Z","subjects":["Computer science","Artificial intelligence"],"languages":["en"],"rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10315/41637","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zylberberg, Joel"]},{"key":"dc:creator","label":"Author","values":["Heyman, Andrew Baldwin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-12-08T14:28:09Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-08T14:28:09Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-12-08"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer science","Artificial intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10315/41637"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Neural network pruning is a popular approach to reducing the computational costs of training and/or deploying a network, and aims to do so while minimizing accuracy loss. Pruning methods that remove individual weights (fine granularity) yield better ratios of accuracy to parameter count, while methods that preserve some or all of a network’s structure (coarser granularity, e.g. pruning channels from a CNN) take better advantage of hardware and software optimized for dense matrix computations. We compare intelligent iterative pruning using several different criteria sampled from the literature against random pruning at initialization across multiple granularities on two different image classification architectures and tasks. We find that the advantage of intelligent pruning (with any criterion) over random pruning decreases dramatically as granularity becomes coarser. Our results suggest that, compared to coarse pruning, fine pruning combined with efficient implementation of the resulting networks is a more promising direction for improving accuracy-to-cost ratios."]},{"key":"dc:title","label":"Title","values":["Fine Granularity is Critical for Intelligent Neural Network Pruning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zylberberg, Joel"],"dc:creator":["Heyman, Andrew Baldwin"],"dc:date.accessioned":["2023-12-08T14:28:09Z"],"dc:date.available":["2023-12-08T14:28:09Z"],"dc:date.issued":["2023-12-08"],"dc:description.abstract":["Neural network pruning is a popular approach to reducing the computational costs of training and/or deploying a network, and aims to do so while minimizing accuracy loss. Pruning methods that remove individual weights (fine granularity) yield better ratios of accuracy to parameter count, while methods that preserve some or all of a network’s structure (coarser granularity, e.g. pruning channels from a CNN) take better advantage of hardware and software optimized for dense matrix computations. We compare intelligent iterative pruning using several different criteria sampled from the literature against random pruning at initialization across multiple granularities on two different image classification architectures and tasks. We find that the advantage of intelligent pruning (with any criterion) over random pruning decreases dramatically as granularity becomes coarser. Our results suggest that, compared to coarse pruning, fine pruning combined with efficient implementation of the resulting networks is a more promising direction for improving accuracy-to-cost ratios."],"dc:identifier.uri":["https://hdl.handle.net/10315/41637"],"dc:language":["en"],"dc:rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"dc:subject":["Computer science","Artificial intelligence"],"dc:title":["Fine Granularity is Critical for Intelligent Neural Network Pruning"],"dc:type":["Electronic Thesis or Dissertation"]},"updated_at":"2026-07-24T06:34:03Z"}