{"id":"https://openalex.org/W4286750853","doi":"https://doi.org/10.48550/arxiv.2207.10083","title":"Mixed-Precision Inference Quantization: Radically Towards Faster inference speed, Lower Storage requirement, and Lower Loss","display_name":"Mixed-Precision Inference Quantization: Radically Towards Faster inference speed, Lower Storage requirement, and Lower Loss","publication_year":2022,"publication_date":"2022-07-20","ids":{"openalex":"https://openalex.org/W4286750853","doi":"https://doi.org/10.48550/arxiv.2207.10083"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2207.10083","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10083","pdf_url":"https://arxiv.org/pdf/2207.10083","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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.10083","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010944123","display_name":"Daning Cheng","orcid":"https://orcid.org/0000-0002-9217-6980"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cheng, Daning","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103141832","display_name":"Wenguang Chen","orcid":"https://orcid.org/0000-0002-4281-1018"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wenguang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5010944123"],"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":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9965999722480774,"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":0.9965999722480774,"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":0.9922999739646912,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9884999990463257,"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/quantization","display_name":"Quantization (signal processing)","score":0.8487746119499207},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7709627747535706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6581173539161682},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5052698254585266},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4503560960292816},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4158437252044678},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29451531171798706},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.11151763796806335}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.8487746119499207},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7709627747535706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6581173539161682},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5052698254585266},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4503560960292816},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4158437252044678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29451531171798706},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.11151763796806335}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2207.10083","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10083","pdf_url":"https://arxiv.org/pdf/2207.10083","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":"doi:10.48550/arxiv.2207.10083","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2207.10083","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2207.10083","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10083","pdf_url":"https://arxiv.org/pdf/2207.10083","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"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/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W2097707447"],"abstract_inverted_index":{"Based":[0],"on":[1,24],"the":[2,30,34,62,68,73,76,83,86,104,113],"model's":[3],"resilience":[4],"to":[5,103,111],"computational":[6],"noise,":[7],"model":[8,36,56],"quantization":[9,20,35,55,105],"is":[10,79,108],"important":[11],"for":[12,51],"compressing":[13],"models":[14],"and":[15,26],"improving":[16],"computing":[17],"speed.":[18],"Existing":[19],"techniques":[21],"rely":[22],"heavily":[23],"experience":[25],"\"fine-tuning\"":[27],"skills.":[28],"In":[29,66,89],"majority":[31],"of":[32,85,115],"instances,":[33],"has":[37],"a":[38,42,49,53,58],"larger":[39],"loss":[40,60,77],"than":[41,61],"full":[43,63],"precision":[44,64],"model.":[45,65],"This":[46],"study":[47],"provides":[48],"methodology":[50],"acquiring":[52],"mixed-precise":[54],"with":[57,97],"lower":[59],"addition,":[67],"analysis":[69],"demonstrates":[70],"that,":[71],"throughout":[72],"inference":[74],"process,":[75],"function":[78],"mostly":[80],"affected":[81],"by":[82],"noise":[84],"layer":[87],"inputs.":[88],"particular,":[90],"we":[91],"will":[92],"demonstrate":[93],"that":[94],"neural":[95],"networks":[96,117],"massive":[98],"identity":[99],"mappings":[100],"are":[101],"resistant":[102],"method.":[106],"It":[107],"also":[109],"difficult":[110],"improve":[112],"performance":[114],"these":[116],"using":[118],"quantization.":[119]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}