{"id":"https://openalex.org/W3079837593","doi":"https://doi.org/10.1109/iske47853.2019.9170341","title":"Accelerating the Forward Convnets with Software Pipeline","display_name":"Accelerating the Forward Convnets with Software Pipeline","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3079837593","doi":"https://doi.org/10.1109/iske47853.2019.9170341","mag":"3079837593"},"language":"en","primary_location":{"id":"doi:10.1109/iske47853.2019.9170341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske47853.2019.9170341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041667917","display_name":"Peng Wu","orcid":"https://orcid.org/0000-0002-7175-6679"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Wu","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,School of Computer,NanJing,China","School of Computer, Nanjing University of Posts and Telecommunications, NanJing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,School of Computer,NanJing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"School of Computer, Nanjing University of Posts and Telecommunications, NanJing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020894075","display_name":"Ningning Zhou","orcid":"https://orcid.org/0000-0001-8530-2785"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ningning Zhou","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,School of Computer,NanJing,China","School of Computer, Nanjing University of Posts and Telecommunications, NanJing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,School of Computer,NanJing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"School of Computer, Nanjing University of Posts and Telecommunications, NanJing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"817","last_page":"821"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10320","display_name":"Neural Networks and Applications","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9954000115394592,"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/computer-science","display_name":"Computer science","score":0.8710349798202515},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.781670331954956},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7267194390296936},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.6012120246887207},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5700183510780334},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5696232914924622},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5262274742126465},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.46326449513435364},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41501691937446594},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4127539098262787},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.36602944135665894},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3505553603172302},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2195296287536621},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.10642576217651367}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8710349798202515},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.781670331954956},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7267194390296936},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.6012120246887207},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5700183510780334},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5696232914924622},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5262274742126465},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.46326449513435364},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41501691937446594},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4127539098262787},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.36602944135665894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3505553603172302},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2195296287536621},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.10642576217651367},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iske47853.2019.9170341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske47853.2019.9170341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1903029394","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2132737349","https://openalex.org/W2163605009","https://openalex.org/W2200374509","https://openalex.org/W2302255633","https://openalex.org/W2888429796","https://openalex.org/W2910681974","https://openalex.org/W2915161590","https://openalex.org/W2963125010","https://openalex.org/W2963390885","https://openalex.org/W2963446712","https://openalex.org/W2981985696","https://openalex.org/W2990503944","https://openalex.org/W6637373629","https://openalex.org/W6640054144","https://openalex.org/W6679393576","https://openalex.org/W6684191040","https://openalex.org/W6687567705","https://openalex.org/W6698183232","https://openalex.org/W6753701838","https://openalex.org/W6756911974","https://openalex.org/W6758091697"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2076915000","https://openalex.org/W2375218795","https://openalex.org/W2393010557"],"abstract_inverted_index":{"We":[0,23,142],"present":[1],"a":[2,34,37,76,99,130],"new":[3],"approach":[4],"to":[5,58,86,137],"accelerate":[6,59,138],"the":[7,28,43,50,62,68,82,87,120,139,151,159,162],"forward":[8,69,140],"convolutional":[9],"neural":[10],"netWorks":[11],"(ConvNets).":[12],"Instead":[13],"of":[14,30,45,52,64,161],"taking":[15],"data":[16],"parallelism":[17],"into":[18],"account":[19],"for":[20],"faster":[21],"inference,":[22],"speed":[24],"up":[25],"ConvNets":[26],"With":[27,61,102],"technology":[29],"software":[31,65,126],"pipeline.":[32,66,127],"Since":[33],"ConvNet":[35,121,146],"is":[36,56,79,129],"chain-like":[38],"model,":[39],"Which":[40],"means":[41],"that":[42,119,150],"output":[44],"current":[46],"layer":[47],"Would":[48,97,110],"become":[49],"input":[51],"next":[53],"layer,":[54,73,92],"it":[55,60,144],"natural":[57],"use":[63],"Firstly,":[67],"propagation":[70],"in":[71,90,113],"each":[72,91],"treated":[74],"as":[75],"minimum":[77],"unit,":[78],"timed":[80],"during":[81],"inference.":[83],"Then,":[84],"according":[85],"computation":[88],"time":[89],"one":[93],"or":[94],"several":[95],"units":[96],"compose":[98],"working":[100],"part":[101],"an":[103],"allocation":[104],"algorithm.":[105],"Finally,":[106],"these":[107],"Working":[108],"parts":[109],"be":[111,123],"executed":[112],"different":[114],"computing":[115],"resource":[116],"simultaneously,":[117],"so":[118],"could":[122,153],"operated":[124],"by":[125],"This":[128],"conceptually":[131],"simple,":[132],"flexible":[133],"and":[134,157],"general":[135],"Way":[136],"ConvNet,":[141],"named":[143],"pipeline":[145],"(PipeCNN).":[147],"Experiments":[148],"show":[149],"method":[152],"obtain":[154],"good":[155],"speedup":[156],"improve":[158],"throughput":[160],"networks.":[163]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}