{"id":"https://openalex.org/W4410887396","doi":"https://doi.org/10.1109/isqed65160.2025.11014471","title":"Deep Neural Network Inference Partitioning in Embedded Hybrid Analog-Digital Systems","display_name":"Deep Neural Network Inference Partitioning in Embedded Hybrid Analog-Digital Systems","publication_year":2025,"publication_date":"2025-04-23","ids":{"openalex":"https://openalex.org/W4410887396","doi":"https://doi.org/10.1109/isqed65160.2025.11014471"},"language":"en","primary_location":{"id":"doi:10.1109/isqed65160.2025.11014471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed65160.2025.11014471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 26th International Symposium on Quality Electronic Design (ISQED)","raw_type":"proceedings-article"},"type":"article","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/A5040618617","display_name":"Fabian Kre\u00df","orcid":"https://orcid.org/0000-0002-1700-5778"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Kre\u00df","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021252999","display_name":"Julian Hoefer","orcid":"https://orcid.org/0000-0003-4904-0495"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Julian Hoefer","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115589101","display_name":"Q. Lin","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":"Qiushi Lin","raw_affiliation_strings":["Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083696598","display_name":"Patrick Schmidt","orcid":"https://orcid.org/0000-0002-8727-6127"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Patrick Schmidt","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068626165","display_name":"Zhenhua Zhu","orcid":"https://orcid.org/0000-0002-4554-1770"},"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":"Zhenhua Zhu","raw_affiliation_strings":["Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673751","display_name":"Yu Zhu","orcid":"https://orcid.org/0000-0003-1535-6520"},"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":"Yu Zhu","raw_affiliation_strings":["Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053259767","display_name":"Tanja Harbaum","orcid":"https://orcid.org/0000-0001-7310-567X"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tanja Harbaum","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445217","display_name":"Yu Wang","orcid":"https://orcid.org/0000-0002-4788-8655"},"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":"Yu Wang","raw_affiliation_strings":["Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025800213","display_name":"J\u00fcrgen Becker","orcid":null},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J\u00fcrgen Becker","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT),Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7588,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86701785,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.894599974155426,"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":0.894599974155426,"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/T11429","display_name":"Semiconductor Lasers and Optical Devices","score":0.8849999904632568,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.8755999803543091,"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":0.7181573510169983},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6741372346878052},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6555389165878296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5184913873672485}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7181573510169983},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6741372346878052},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6555389165878296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5184913873672485}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isqed65160.2025.11014471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed65160.2025.11014471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 26th International Symposium on Quality Electronic Design (ISQED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2234584938","https://openalex.org/W2940862705","https://openalex.org/W2945146780","https://openalex.org/W2946659370","https://openalex.org/W2980104813","https://openalex.org/W2998732502","https://openalex.org/W3003257820","https://openalex.org/W3021613070","https://openalex.org/W3108426037","https://openalex.org/W3161584439","https://openalex.org/W4293024010","https://openalex.org/W4296209134","https://openalex.org/W4313467202","https://openalex.org/W4323022446","https://openalex.org/W4361857408","https://openalex.org/W4362723189","https://openalex.org/W4379115852","https://openalex.org/W4379115905","https://openalex.org/W4386859301","https://openalex.org/W4391594506","https://openalex.org/W4392745562","https://openalex.org/W4394909859","https://openalex.org/W6695314431"],"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":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs)":[3],"deployed":[4],"in":[5,109,163,172],"resource-constrained":[6],"embedded":[7],"systems,":[8],"like":[9],"automotive":[10],"platforms,":[11],"often":[12],"face":[13],"severe":[14],"\u201cmemory":[15,50],"wall\u201d":[16,51],"problems":[17],"and":[18,74,92,135,142],"energy-consuming":[19],"data":[20],"movements.":[21],"To":[22],"address":[23],"this":[24,96],"challenge,":[25],"existing":[26],"work":[27],"has":[28,79],"proposed":[29],"Processing-In-Memory":[30],"(PIM)":[31],"architectures":[32],"based":[33,69],"on":[34,70,154],"emerging":[35],"non-volatile":[36],"memory,":[37,43],"which":[38],"perform":[39],"analog-domain":[40],"computations":[41],"inside":[42],"offering":[44],"great":[45],"potentials":[46],"to":[47,103,126,167],"solve":[48],"the":[49,121,145],"problem.":[52],"However,":[53],"PIM-based":[54],"accelerators":[55],"suffer":[56],"from":[57],"circuit":[58],"noise,":[59],"impacting":[60],"DNN":[61,83,106,158],"accuracy.":[62,94],"Therefore,":[63],"building":[64],"a":[65,81,116,155,161,170],"heterogeneous":[66],"multi-chiplet":[67,112],"architecture":[68],"energy-efficient":[71],"analog":[72,132],"PIM":[73,133],"high-precision":[75],"digital":[76,136],"computing":[77],"units":[78],"become":[80],"promising":[82],"acceleration":[84],"solution":[85],"that":[86],"achieves":[87],"both":[88],"high":[89,93],"energy":[90],"efficiency":[91],"In":[95],"paper,":[97],"we":[98],"present":[99],"an":[100],"automated":[101],"framework":[102,146],"explore":[104],"layer-wise":[105],"inference":[107],"partitioning":[108,149],"hybrid":[110],"analog-digital":[111],"systems.":[113],"After":[114],"performing":[115],"topological":[117],"ordering,":[118],"it":[119],"analyzes":[120],"robustness":[122],"of":[123,157,165,174],"each":[124],"layer":[125],"constrain":[127],"mapping":[128],"decisions":[129],"across":[130],"available":[131],"chiplets":[134],"chiplets.":[137],"By":[138],"considering":[139],"several":[140],"functional":[141],"performance":[143],"metrics,":[144],"identifies":[147],"Pareto-optimal":[148],"schemes.":[150],"Exten-sive":[151],"experimental":[152],"results":[153],"variety":[156],"models":[159],"show":[160],"reduction":[162],"latency":[164],"up":[166],"52%":[168],"with":[169],"loss":[171],"accuracy":[173],"less":[175],"than":[176],"1%.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
