{"id":"https://openalex.org/W4377864779","doi":"https://doi.org/10.1145/3613424.3623786","title":"HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity","display_name":"HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity","publication_year":2023,"publication_date":"2023-10-28","ids":{"openalex":"https://openalex.org/W4377864779","doi":"https://doi.org/10.1145/3613424.3623786"},"language":"en","primary_location":{"id":"doi:10.1145/3613424.3623786","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3613424.3623786","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3613424.3623786","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"56th Annual IEEE/ACM International Symposium on Microarchitecture","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3613424.3623786","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050803970","display_name":"Yannan Nellie Wu","orcid":"https://orcid.org/0009-0001-0933-4600"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yannan Nellie Wu","raw_affiliation_strings":["Massachusetts Institute of Technology, United States of America"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, United States of America","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080155613","display_name":"Po-An Tsai","orcid":"https://orcid.org/0000-0003-4561-6450"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Po-An Tsai","raw_affiliation_strings":["NVIDIA, United States of America"],"affiliations":[{"raw_affiliation_string":"NVIDIA, United States of America","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033185286","display_name":"Saurav Muralidharan","orcid":"https://orcid.org/0000-0003-4024-3958"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saurav Muralidharan","raw_affiliation_strings":["NVIDIA, United States of America"],"affiliations":[{"raw_affiliation_string":"NVIDIA, United States of America","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024901904","display_name":"Angshuman Parashar","orcid":"https://orcid.org/0000-0001-9936-6501"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Angshuman Parashar","raw_affiliation_strings":["NVIDIA, United States of America"],"affiliations":[{"raw_affiliation_string":"NVIDIA, United States of America","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066351389","display_name":"Vivienne Sze","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vivienne Sze","raw_affiliation_strings":["Massachusetts Institute of Technology, United States of America"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, United States of America","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024384625","display_name":"Joel Emer","orcid":null},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]},{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joel Emer","raw_affiliation_strings":["Massachusetts Institute of Technology, United States of America and NVIDIA, USA"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, United States of America and NVIDIA, USA","institution_ids":["https://openalex.org/I4210127875","https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5050803970"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":3.1178,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.9331384,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1106","last_page":"1120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.7987056970596313},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7242863178253174},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.367258757352829},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36377203464508057}],"concepts":[{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.7987056970596313},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7242863178253174},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.367258757352829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36377203464508057},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3613424.3623786","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3613424.3623786","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3613424.3623786","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"56th Annual IEEE/ACM International Symposium on Microarchitecture","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2305.12718","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.12718","pdf_url":"https://arxiv.org/pdf/2305.12718","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:dspace.mit.edu:1721.1/153277","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/153277","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/153277/1/3613424.3623786.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"doi:10.1145/3613424.3623786","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3613424.3623786","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3613424.3623786","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"56th Annual IEEE/ACM International Symposium on Microarchitecture","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8600000143051147}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4377864779.pdf","grobid_xml":"https://content.openalex.org/works/W4377864779.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1884140786","https://openalex.org/W1994878318","https://openalex.org/W2108598243","https://openalex.org/W2148831941","https://openalex.org/W2152839228","https://openalex.org/W2181200309","https://openalex.org/W2289252105","https://openalex.org/W2442974303","https://openalex.org/W2512924740","https://openalex.org/W2625457103","https://openalex.org/W2794952988","https://openalex.org/W2900228909","https://openalex.org/W2940862705","https://openalex.org/W2945146780","https://openalex.org/W2949619037","https://openalex.org/W2949674408","https://openalex.org/W2953212265","https://openalex.org/W2963363373","https://openalex.org/W2979310060","https://openalex.org/W2979439447","https://openalex.org/W2980113464","https://openalex.org/W2980186997","https://openalex.org/W2996874060","https://openalex.org/W2998732502","https://openalex.org/W3016542674","https://openalex.org/W3016832937","https://openalex.org/W3037261029","https://openalex.org/W3045965140","https://openalex.org/W3104849992","https://openalex.org/W3106070274","https://openalex.org/W3129093240","https://openalex.org/W3135807226","https://openalex.org/W3153174751","https://openalex.org/W3155922894","https://openalex.org/W3185702163","https://openalex.org/W3187481008","https://openalex.org/W3187908937","https://openalex.org/W3190761184","https://openalex.org/W3196410085","https://openalex.org/W4221157729","https://openalex.org/W4285130446","https://openalex.org/W4287363917","https://openalex.org/W4327930517","https://openalex.org/W4389500477"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829"],"abstract_inverted_index":{"Due":[0],"to":[1,46],"complex":[2],"interactions":[3],"among":[4],"various":[5],"deep":[6],"neural":[7],"network":[8],"(DNN)":[9],"optimization":[10],"techniques,":[11],"modern":[12],"DNNs":[13],"can":[14],"have":[15,43],"weights":[16],"and":[17,35],"activations":[18],"that":[19],"are":[20],"dense":[21],"or":[22],"sparse":[23],"with":[24],"diverse":[25],"sparsity":[26,50],"degrees.":[27],"To":[28],"offer":[29],"a":[30],"good":[31],"trade-off":[32],"between":[33],"accuracy":[34],"hardware":[36],"performance,":[37],"an":[38],"ideal":[39],"DNN":[40,49],"accelerator":[41],"should":[42],"high":[44],"flexibility":[45],"efficiently":[47],"translate":[48],"into":[51],"reductions":[52],"in":[53],"energy":[54],"and/or":[55],"latency":[56],"without":[57],"incurring":[58],"significant":[59],"complexity":[60],"overhead.":[61]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}