{"id":"https://openalex.org/W4413014923","doi":"https://doi.org/10.1145/3676642.3736118","title":"Syno: Structured Synthesis for Neural Operators","display_name":"Syno: Structured Synthesis for Neural Operators","publication_year":2025,"publication_date":"2025-08-06","ids":{"openalex":"https://openalex.org/W4413014923","doi":"https://doi.org/10.1145/3676642.3736118"},"language":"en","primary_location":{"id":"doi:10.1145/3676642.3736118","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3676642.3736118","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3676642.3736118","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3676642.3736118","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yongqi Zhuo","orcid":"https://orcid.org/0009-0004-2229-2750"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongqi Zhuo","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-2229-2750","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108846632","display_name":"Zhengyuan Su","orcid":"https://orcid.org/0009-0003-0121-2128"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengyuan Su","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-0121-2128","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101100042","display_name":"Chenggang Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenggang Zhao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-2297-0790","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026297396","display_name":"Mingyu Gao","orcid":"https://orcid.org/0000-0001-8433-7281"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyu Gao","raw_affiliation_strings":["Tsinghua University, Beijing, China, Shanghai Artificial Intelligence Lab, Shanghai, China, and Shanghai Qi Zhi Institute, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-8433-7281","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China, Shanghai Artificial Intelligence Lab, Shanghai, China, and Shanghai Qi Zhi Institute, Shanghai, China","institution_ids":["https://openalex.org/I4210100255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15035433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"212","last_page":"229"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9987000226974487,"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/T10320","display_name":"Neural Networks and Applications","score":0.9987000226974487,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.982699990272522,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9793000221252441,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6590546369552612},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4404713809490204},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36100590229034424}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6590546369552612},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4404713809490204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36100590229034424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3676642.3736118","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3676642.3736118","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3676642.3736118","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3676642.3736118","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3676642.3736118","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3676642.3736118","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413014923.pdf","grobid_xml":"https://content.openalex.org/works/W4413014923.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W2055312318","https://openalex.org/W2108598243","https://openalex.org/W2152397470","https://openalex.org/W2194775991","https://openalex.org/W2549139847","https://openalex.org/W2560674852","https://openalex.org/W2611669587","https://openalex.org/W2885311373","https://openalex.org/W2954698171","https://openalex.org/W2963446712","https://openalex.org/W2963821229","https://openalex.org/W2963844898","https://openalex.org/W2963918968","https://openalex.org/W2964081807","https://openalex.org/W2967733054","https://openalex.org/W2977371611","https://openalex.org/W3035363850","https://openalex.org/W3100341797","https://openalex.org/W3110583370","https://openalex.org/W3156810276","https://openalex.org/W3177452048","https://openalex.org/W4220713611","https://openalex.org/W4220929067","https://openalex.org/W4229675450","https://openalex.org/W4281710230","https://openalex.org/W4307886645","https://openalex.org/W4394998532","https://openalex.org/W6753278433"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0],"desires":[1],"for":[2],"better":[3,76,164],"prediction":[4],"accuracy":[5,77,184],"and":[6,19,69,115,146,176],"higher":[7],"execution":[8],"performance":[9],"in":[10],"neural":[11,62,73,89],"networks":[12],"never":[13],"end.":[14],"Neural":[15],"architecture":[16],"search":[17,151],"(NAS)":[18],"tensor":[20,104],"compilers":[21],"are":[22,33],"two":[23,29],"popular":[24],"techniques":[25,120],"to":[26,36,67,86,111,121,135,153,171],"optimize":[27],"these":[28],"goals,":[30],"but":[31],"they":[32],"both":[34],"limited":[35],"composing":[37],"or":[38],"optimizing":[39],"existing":[40],"manually":[41],"designed":[42],"operators":[43,74,138,165],"rather":[44],"than":[45,182],"coming":[46],"up":[47],"with":[48,75,140,166],"completely":[49],"new":[50],"designs.":[51],"In":[52],"this":[53],"work,":[54],"we":[55],"explore":[56,155],"the":[57,141,156],"less":[58,181],"studied":[59],"direction":[60],"of":[61,95,99,169],"operator":[63,90],"synthesis,":[64],"which":[65,106],"aims":[66],"automatically":[68],"efficiently":[70],"discover":[71],"novel":[72,97,131],"and/or":[78],"speed.":[79],"We":[80,159],"develop":[81],"an":[82],"end-to-end":[83],"framework":[84],"Syno,":[85],"realize":[87],"practical":[88],"synthesis.":[91],"Syno":[92,127,162],"makes":[93],"use":[94],"a":[96,130],"set":[98],"fine-grained":[100],"primitives":[101],"defined":[102],"on":[103,173,187],"dimensions,":[105],"ensure":[107],"various":[108,174],"desired":[109],"properties":[110],"ease":[112],"model":[113],"training,":[114],"also":[116],"enable":[117],"expression":[118],"canonicalization":[119],"avoid":[122],"redundant":[123],"candidates":[124],"during":[125],"search.":[126],"further":[128],"adopts":[129],"guided":[132],"synthesis":[133],"flow":[134],"obtain":[136],"valid":[137],"matched":[139],"specified":[142],"input/output":[143],"dimension":[144],"sizes,":[145],"leverages":[147],"efficient":[148],"stochastic":[149],"tree":[150],"algorithms":[152],"quickly":[154],"design":[157],"space.":[158],"demonstrate":[160],"that":[161],"discovers":[163],"average":[167],"speedups":[168],"1.37\u00d7":[170],"2.06\u00d7":[172],"hardware":[175],"compiler":[177],"choices,":[178],"while":[179],"keeping":[180],"1%":[183],"loss":[185],"even":[186],"NAS-optimized":[188],"models.":[189]},"counts_by_year":[],"updated_date":"2026-08-22T07:34:49.880490","created_date":"2025-10-10T00:00:00"}
