{"id":"https://openalex.org/W6891851770","doi":"https://doi.org/10.48550/arxiv.2501.07767","title":"HgPCN: A Heterogeneous Architecture for E2E Embedded Point Cloud Inference","display_name":"HgPCN: A Heterogeneous Architecture for E2E Embedded Point Cloud Inference","publication_year":2025,"publication_date":"2025-01-14","ids":{"openalex":"https://openalex.org/W6891851770","doi":"https://doi.org/10.48550/arxiv.2501.07767"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2501.07767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2501.07767","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2501.07767","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gao, Yiming","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gao, Yiming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Jiang, Chao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Chao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Piard, Wesley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Piard, Wesley","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Xiangru","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiangru","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Patel, Bhavesh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patel, Bhavesh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Lam, Herman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lam, Herman","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.08129999786615372,"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"}},"topics":[{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.08129999786615372,"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/T11873","display_name":"PAPR reduction in OFDM","score":0.039000000804662704,"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/T10320","display_name":"Neural Networks and Applications","score":0.03720000013709068,"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/cloud-computing","display_name":"Cloud computing","score":0.758400022983551},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.647599995136261},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.595300018787384},{"id":"https://openalex.org/keywords/structuring","display_name":"Structuring","score":0.49790000915527344},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.4869999885559082},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.34850001335144043},{"id":"https://openalex.org/keywords/skyline","display_name":"Skyline","score":0.33799999952316284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7739999890327454},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.758400022983551},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.647599995136261},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.595300018787384},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5626999735832214},{"id":"https://openalex.org/C2775945657","wikidata":"https://www.wikidata.org/wiki/Q381442","display_name":"Structuring","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4869999885559082},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C2780757406","wikidata":"https://www.wikidata.org/wiki/Q465837","display_name":"Skyline","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32109999656677246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3188000023365021},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.303600013256073},{"id":"https://openalex.org/C120115606","wikidata":"https://www.wikidata.org/wiki/Q5135723","display_name":"Cloud testing","level":4,"score":0.30320000648498535},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2501.07767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2501.07767","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2501.07767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2501.07767","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.42785465717315674,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Point":[0,22],"cloud":[1,38,51,73,86,97,135,173],"is":[2,201,229],"an":[3,66,165,198],"important":[4],"type":[5],"of":[6,70,76,95,111,121,132,196,209,241],"geometric":[7],"data":[8,123,149,243],"structure":[9],"for":[10,129,152,169],"many":[11],"embedded":[12,171],"applications":[13,52],"such":[14],"as":[15],"autonomous":[16],"driving":[17],"and":[18,46,81,147],"augmented":[19],"reality.":[20],"Current":[21,88],"Cloud":[23],"Networks":[24],"(PCNs)":[25],"have":[26],"proven":[27],"to":[28,35,83,91,115,186,203,236],"achieve":[29],"great":[30],"success":[31],"in":[32,143,155,246],"using":[33],"inference":[34,62,82,157,248],"perform":[36,84],"point":[37,50,72,85,96,134,172],"analysis,":[39],"including":[40],"object":[41],"part":[42],"segmentation,":[43],"shape":[44],"classification,":[45],"so":[47],"on.":[48],"However,":[49],"on":[53,105,183,231],"the":[54,61,71,101,106,109,112,119,133,144,148,156,188,193,205,210,214,239,242,247],"computing":[55],"edge":[56],"require":[57,65],"more":[58],"than":[59],"just":[60],"step.":[63],"They":[64],"end-to-end":[67,93,130,166],"(E2E)":[68],"processing":[69,94,131],"workloads:":[74],"pre-processing":[75,145,211],"raw":[77,122],"data,":[78],"input":[79,153],"preparation,":[80],"analysis.":[87],"PCN":[89],"approaches":[90],"support":[92],"workload":[98,240],"cannot":[99],"meet":[100],"real-time":[102,170],"latency":[103],"requirement":[104],"edge,":[107],"i.e.,":[108],"ability":[110],"AI":[113],"service":[114],"keep":[116],"up":[117],"with":[118,222],"speed":[120],"generation":[124],"by":[125],"3D":[126],"sensors.":[127],"Latency":[128],"workloads":[136],"stems":[137],"from":[138],"two":[139,179,189],"reasons:":[140],"memory-intensive":[141,206],"down-sampling":[142,207],"phase":[146],"structuring":[150,244],"step":[151,245],"preparation":[154],"phase.":[158,212,249],"In":[159,175,192,213],"this":[160],"paper,":[161],"we":[162,177],"present":[163],"HgPCN,":[164,176,197],"heterogeneous":[167],"architecture":[168],"applications.":[174],"introduce":[178],"novel":[180],"methodologies":[181],"based":[182,230],"spatial":[184],"indexing":[185],"address":[187],"identified":[190],"bottlenecks.":[191],"Pre-processing":[194],"Engine":[195],"Octree-Indexed-Sampling":[199],"method":[200,235],"used":[202],"optimize":[204],"bottleneck":[208],"Inference":[215],"Engine,":[216],"HgPCN":[217],"extends":[218],"a":[219,223,232],"commercial":[220],"DLA":[221],"customized":[224],"Data":[225],"Structuring":[226],"Unit":[227],"which":[228],"Voxel-Expanded":[233],"Gathering":[234],"fundamentally":[237],"reduce":[238]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}