{"id":"https://openalex.org/W4417131010","doi":"https://doi.org/10.1109/access.2025.3641821","title":"Toward Approximate Computing for Deep Learning in Embedded Systems: A Systematic Literature Review","display_name":"Toward Approximate Computing for Deep Learning in Embedded Systems: A Systematic Literature Review","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4417131010","doi":"https://doi.org/10.1109/access.2025.3641821"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3641821","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3641821","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3641821","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026526820","display_name":"Yawar Rasheed","orcid":"https://orcid.org/0000-0002-9557-997X"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Y. Rasheed","raw_affiliation_strings":["Faculty of EEMCS, University of Twente, Enschede, The Netherlands","Faculty of EEMCS, University of Twente, Enschede, Netherlands"],"affiliations":[{"raw_affiliation_string":"Faculty of EEMCS, University of Twente, Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"Faculty of EEMCS, University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045766368","display_name":"Muhammad Waseem Anwar","orcid":"https://orcid.org/0000-0002-1193-5683"},"institutions":[{"id":"https://openalex.org/I2799978770","display_name":"X-Fab (Germany)","ror":"https://ror.org/030bh9196","country_code":"DE","type":"company","lineage":["https://openalex.org/I2799978770"]},{"id":"https://openalex.org/I82509713","display_name":"M\u00e4lardalen University","ror":"https://ror.org/033vfbz75","country_code":"SE","type":"education","lineage":["https://openalex.org/I82509713"]}],"countries":["DE","SE"],"is_corresponding":false,"raw_author_name":"M. W. Anwar","raw_affiliation_strings":["Division of Computer Science and Software Engineering, Mälardalen University, Västerås, Sweden","Division of Computer Science and Software Engineering, Mälardalen University Västerås, Sweden"],"affiliations":[{"raw_affiliation_string":"Division of Computer Science and Software Engineering, Mälardalen University, Västerås, Sweden","institution_ids":["https://openalex.org/I82509713"]},{"raw_affiliation_string":"Division of Computer Science and Software Engineering, Mälardalen University Västerås, Sweden","institution_ids":["https://openalex.org/I2799978770"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034253211","display_name":"G. A. Gillani","orcid":"https://orcid.org/0000-0002-2850-4265"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"G. A. Gillani","raw_affiliation_strings":["Faculty of EEMCS, University of Twente, Enschede, The Netherlands","Faculty of EEMCS, University of Twente, Enschede, Netherlands"],"affiliations":[{"raw_affiliation_string":"Faculty of EEMCS, University of Twente, Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"Faculty of EEMCS, University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048232172","display_name":"Marco Ottavi","orcid":"https://orcid.org/0000-0002-5064-7342"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"M. Ottavi","raw_affiliation_strings":["Faculty of EEMCS, University of Twente, Enschede, The Netherlands","Faculty of EEMCS, University of Twente, Enschede, Netherlands"],"affiliations":[{"raw_affiliation_string":"Faculty of EEMCS, University of Twente, Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"Faculty of EEMCS, University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026526820"],"corresponding_institution_ids":["https://openalex.org/I94624287"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38753486,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"210863","last_page":"210891"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.5509999990463257,"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"}},"topics":[{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.5509999990463257,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.13840000331401825,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.07689999788999557,"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/mnist-database","display_name":"MNIST database","score":0.7218999862670898},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7116000056266785},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7059999704360962},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6223999857902527},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.6055999994277954},{"id":"https://openalex.org/keywords/systematic-review","display_name":"Systematic review","score":0.5540000200271606},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.5392000079154968}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7695000171661377},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7218999862670898},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7116000056266785},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7059999704360962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6274999976158142},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6223999857902527},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6055999994277954},{"id":"https://openalex.org/C189708586","wikidata":"https://www.wikidata.org/wiki/Q1504425","display_name":"Systematic review","level":3,"score":0.5540000200271606},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.491100013256073},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2754000127315521},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26969999074935913},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.260699987411499},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/access.2025.3641821","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3641821","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:ris.utwente.nl:openaire/1b7923dc-8761-4c67-aa05-b01b27651f78","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/1b7923dc-8761-4c67-aa05-b01b27651f78","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Rasheed, Y, Anwar, M W, Gillani, G A & Ottavi, M 2025, 'Toward Approximate Computing for Deep Learning in Embedded Systems : A Systematic Literature Review', IEEE Access, vol. 13, pp. 210863-210891. https://doi.org/10.1109/ACCESS.2025.3641821","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:art.torvergata.it:2108/453266","is_oa":false,"landing_page_url":"https://hdl.handle.net/2108/453266","pdf_url":null,"source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"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":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:87f28c8a039742aea644906259d88fd4","is_oa":true,"landing_page_url":"https://doaj.org/article/87f28c8a039742aea644906259d88fd4","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 210863-210891 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3641821","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3641821","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321015","display_name":"University of Twente","ror":"https://ror.org/006hf6230"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,258],"evolution":[1],"of":[2,11,14,73,85,99,146,260],"Deep":[3],"Learning":[4],"(DL)":[5],"algorithms,":[6],"coupled":[7],"with":[8,177,278,286],"the":[9,69,86,97,147,170,178,187,229,242,268,279],"availability":[10],"vast":[12],"amounts":[13],"data,":[15],"has":[16,43,193],"enabled":[17],"superior":[18],"accuracy":[19,28,57],"across":[20],"multiple":[21,83],"Artificial":[22],"Intelligence":[23],"(AI)":[24],"tasks.":[25],"However,":[26,95],"such":[27],"incurs":[29],"substantial":[30],"computational":[31],"and":[32,66,90,133,165,182,189,204,208,211,225,232,247,270,282],"energy":[33],"costs,":[34],"limiting":[35],"deployment":[36,173],"on":[37,113],"resource-constrained":[38],"embedded":[39,167],"devices.":[40],"Approximate":[41,114,201],"Computing":[42,115],"emerged":[44,227],"as":[45,228],"a":[46,249,264],"promising":[47],"paradigm":[48],"to":[49,96,144,255,272],"mitigate":[50],"this":[51,239,261],"challenge":[52],"by":[53,125],"trading":[54],"off":[55],"tolerable":[56],"loss":[58],"for":[59,116,169,267],"significant":[60],"gains":[61],"in":[62,118,236],"energy,":[63],"power,":[64],"area,":[65],"performance.":[67],"Leveraging":[68],"inherent":[70],"error":[71],"resilience":[72],"DL":[74,87,117,148,163,172,253],"models,":[75,210],"approximation":[76,156,275],"techniques":[77,276],"have":[78],"been":[79,195],"extensively":[80],"explored":[81],"at":[82],"layers":[84,145],"computing":[88,149],"stack,":[89,150],"several":[91],"surveys":[92],"also":[93,194],"exist.":[94],"best":[98],"our":[100],"knowledge,":[101],"no":[102],"prior":[103],"Systematic":[104],"Literature":[105],"Review":[106],"(SLR)":[107],"exists":[108],"that":[109,200],"is":[110],"specifically":[111],"focused":[112],"Embedded":[119],"Systems.":[120],"This":[121,197],"article":[122],"presents":[123],"SLR":[124,198,240,262],"selecting":[126],"51":[127],"studies":[128,137],"published":[129],"between":[130],"January":[131],"2019":[132],"May":[134],"2025.":[135],"These":[136],"are":[138,174,217],"organized":[139],"into":[140],"four":[141],"categories":[142],"corresponding":[143],"including":[151],"cross-layer":[152,251],"approximations.":[153],"Furthermore,":[154],"12":[155],"techniques,":[157],"11":[158],"benchmark":[159],"datasets,":[160,206],"26":[161],"prominent":[162],"architectures,":[164],"three":[166],"platforms":[168],"energy-efficient":[171],"summarized,":[175],"along":[176,277],"leading":[179],"tools,":[180],"frameworks,":[181],"optimization/":[183],"evaluation":[184],"metrics.":[185],"Additionally,":[186],"academic":[188],"industry-wide":[190],"global":[191],"adoption":[192],"highlighted.":[196],"reveals":[199],"Multipliers,":[202],"CIFAR-10":[203],"MNIST":[205],"ResNet-50":[207],"LeNet":[209],"ASIC-based":[212],"45":[213],"nm":[214],"technology":[215],"designs":[216],"most":[218],"frequently":[219],"targeted,":[220],"whereas":[221],"Synopsys":[222],"DC,":[223],"PyTorch,":[224],"Verilog":[226],"predominant":[230],"tools":[231,281],"frameworks":[233],"supporting":[234],"approximations":[235],"DL.":[237],"Finally,":[238],"highlights":[241],"current":[243],"challenges,":[244],"research":[245],"gaps,":[246],"introduces":[248],"conceptual":[250],"approximate":[252],"framework":[254],"address":[256],"them.":[257],"findings":[259],"offer":[263],"comprehensive":[265],"reference":[266],"researchers":[269],"practitioners":[271],"select":[273],"suitable":[274],"underlying":[280],"hardware":[283],"platforms,":[284],"aligned":[285],"their":[287],"application":[288],"requirements.":[289]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-12-08T00:00:00"}