{"id":"https://openalex.org/W2911009130","doi":"https://doi.org/10.1145/3287624.3287642","title":"NeuralHMC","display_name":"NeuralHMC","publication_year":2019,"publication_date":"2019-01-18","ids":{"openalex":"https://openalex.org/W2911009130","doi":"https://doi.org/10.1145/3287624.3287642","mag":"2911009130"},"language":"en","primary_location":{"id":"doi:10.1145/3287624.3287642","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3287624.3287642","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3287624.3287642","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th Asia and South Pacific Design Automation Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3287624.3287642","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086617294","display_name":"Chuhan Min","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chuhan Min","raw_affiliation_strings":["University of Pittsburgh"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102935726","display_name":"Jiachen Mao","orcid":"https://orcid.org/0000-0001-8986-0696"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiachen Mao","raw_affiliation_strings":["Duke University"],"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100429403","display_name":"Hai Li","orcid":"https://orcid.org/0000-0003-3228-6544"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hai Li","raw_affiliation_strings":["Duke University"],"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058073627","display_name":"Yiran Chen","orcid":"https://orcid.org/0000-0002-1486-8412"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiran Chen","raw_affiliation_strings":["Duke University"],"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086617294"],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":1.2105,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.79101539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"394","last_page":"399"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4942045509815216}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4942045509815216}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3287624.3287642","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3287624.3287642","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3287624.3287642","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th Asia and South Pacific Design Automation Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3287624.3287642","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3287624.3287642","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3287624.3287642","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th Asia and South Pacific Design Automation Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9100000262260437}],"awards":[{"id":"https://openalex.org/G1316584150","display_name":"SHF: Small: Cross-Platform Solutions for Pruning and Accelerating Neural Network Models","funder_award_id":"1615475","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1810510998","display_name":null,"funder_award_id":"1615475, 1725456","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4789755018","display_name":null,"funder_award_id":"1725456","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8580749403","display_name":null,"funder_award_id":"DE-SC0018064","funder_id":"https://openalex.org/F4320337674","funder_display_name":"Wind Energy Technologies Office"},{"id":"https://openalex.org/G8940334949","display_name":null,"funder_award_id":"DE-SC0018064","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320337674","display_name":"Wind Energy Technologies Office","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2911009130.pdf","grobid_xml":"https://content.openalex.org/works/W2911009130.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W2094756095","https://openalex.org/W2119108200","https://openalex.org/W2134973740","https://openalex.org/W2147657366","https://openalex.org/W2163605009","https://openalex.org/W2289252105","https://openalex.org/W2488627141","https://openalex.org/W2606722458","https://openalex.org/W2611106620","https://openalex.org/W2738366405","https://openalex.org/W2763227140","https://openalex.org/W2963073614","https://openalex.org/W2964299589","https://openalex.org/W3099610822","https://openalex.org/W4212788319","https://openalex.org/W4254672563"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2093578348","https://openalex.org/W2376932109","https://openalex.org/W2382290278","https://openalex.org/W2350741829","https://openalex.org/W3004735627","https://openalex.org/W2130043461"],"abstract_inverted_index":{"In":[0,72],"Deep":[1],"Neural":[2],"Network":[3],"(DNN)":[4],"applications,":[5],"energy":[6,130],"consumption":[7],"and":[8,17,128],"performance":[9,125],"cost":[10],"of":[11,25,66],"moving":[12],"data":[13,45,94,104],"between":[14],"memory":[15],"hierarchy":[16],"computational":[18],"units":[19],"are":[20],"significantly":[21],"higher":[22],"than":[23],"that":[24,90],"the":[26,44,78,93,102,123],"computation":[27,60],"itself.":[28],"Process-in-memory":[29],"(PIM)":[30],"architecture":[31],"such":[32],"as":[33],"Hybrid":[34],"Memory":[35],"Cube":[36],"(HMC),":[37],"becomes":[38],"an":[39],"excellent":[40],"candidate":[41],"to":[42,55,98,108,114],"improve":[43,122],"locality":[46],"for":[47,83],"efficient":[48,84],"DNN":[49,62,85,103,117],"execution.":[50,86],"However,":[51],"it's":[52],"still":[53],"hard":[54],"efficiently":[56],"deploy":[57],"large-scale":[58],"matrix":[59],"in":[61],"on":[63,101,133],"HMC":[64],"because":[65],"its":[67],"coarse":[68],"grained":[69],"packet":[70],"protocol.":[71],"this":[73],"work,":[74],"we":[75],"propose":[76],"NeuralHMC,":[77],"first":[79],"HMC-based":[80],"accelerator":[81],"tailored":[82],"Experimental":[87],"results":[88],"show":[89],"NeuralHMC":[91,119],"reduces":[92,129],"movement":[95],"by":[96,126,131],"1.4x":[97],"2.5x":[99],"(depending":[100],"reuse":[105],"strategy)":[106],"compared":[107,113],"Von":[109],"Neumann":[110],"architecture.":[111],"Furthermore,":[112],"state-of-the-art":[115],"PIM-based":[116],"accelerator,":[118],"can":[120],"promisingly":[121],"system":[124],"4.1x":[127],"1.5x,":[132],"average.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2019-01-25T00:00:00"}
