{"id":"https://openalex.org/W7155178815","doi":"https://doi.org/10.48550/arxiv.2604.19106","title":"Design Rules for Extreme-Edge Scientific Computing on AI Engines","display_name":"Design Rules for Extreme-Edge Scientific Computing on AI Engines","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155178815","doi":"https://doi.org/10.48550/arxiv.2604.19106"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.19106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19106","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.19106","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134276927","display_name":"Zhenghua Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Zhenghua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134216850","display_name":"G Abarajithan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abarajithan, G","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134311066","display_name":"Dimitrios Danopoulos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Danopoulos, Dimitrios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134232657","display_name":"Olivia Weng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weng, Olivia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134245897","display_name":"Francesco Restuccia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Restuccia, Francesco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5000231774","display_name":"Ryan Kastner","orcid":"https://orcid.org/0000-0001-9062-5570"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kastner, Ryan","raw_affiliation_strings":[],"raw_orcid":null,"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":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4056999981403351,"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":0.4056999981403351,"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.15109999477863312,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.051899999380111694,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dataflow","display_name":"Dataflow","score":0.8654000163078308},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.5103999972343445},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.508400022983551},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.4348999857902527},{"id":"https://openalex.org/keywords/programmable-logic-device","display_name":"Programmable logic device","score":0.351500004529953},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.326200008392334},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.30979999899864197},{"id":"https://openalex.org/keywords/execution-model","display_name":"Execution model","score":0.30559998750686646},{"id":"https://openalex.org/keywords/prolog","display_name":"Prolog","score":0.30390000343322754}],"concepts":[{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.8654000163078308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7462999820709229},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.5103999972343445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.508400022983551},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3691999912261963},{"id":"https://openalex.org/C206274596","wikidata":"https://www.wikidata.org/wiki/Q1063837","display_name":"Programmable logic device","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.30869999527931213},{"id":"https://openalex.org/C2776834041","wikidata":"https://www.wikidata.org/wiki/Q25346349","display_name":"Execution model","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C81721847","wikidata":"https://www.wikidata.org/wiki/Q163468","display_name":"Prolog","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C34165917","wikidata":"https://www.wikidata.org/wiki/Q188267","display_name":"Programming paradigm","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.28859999775886536},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2547999918460846},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25459998846054077},{"id":"https://openalex.org/C176727019","wikidata":"https://www.wikidata.org/wiki/Q1172415","display_name":"Dataflow architecture","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.19106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19106","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.19106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19106","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Extreme-edge":[0],"scientific":[1,118,166],"applications":[2],"use":[3],"machine":[4],"learning":[5],"models":[6,55],"to":[7,51,53,57,164],"analyze":[8],"sensor":[9],"data":[10],"and":[11,18,25,76,85,103,115,135,137,159],"make":[12],"real-time":[13],"decisions.":[14],"Their":[15],"stringent":[16],"latency":[17],"throughput":[19],"requirements":[20],"demand":[21],"small":[22,46],"batch":[23],"sizes":[24],"require":[26],"that":[27,145,181],"model":[28],"weights":[29],"remain":[30],"fully":[31],"on-chip.":[32],"Spatial":[33,41],"dataflow":[34,42,161],"implementations":[35,150],"are":[36],"common":[37],"for":[38,45],"extreme-edge":[39,117],"applications.":[40],"works":[43],"well":[44],"networks,":[47],"but":[48],"it":[49],"fails":[50],"scale":[52],"larger":[54],"due":[56],"inherent":[58],"resource":[59,141],"scaling":[60],"limitations.":[61],"AI":[62,89,108,125,148,179],"Engines":[63,90,109,126,180],"on":[64,124,178,184],"modern":[65],"FPGA":[66],"SoCs":[67],"offer":[68],"a":[69,139],"promising":[70],"alternative":[71],"with":[72],"high":[73],"compute":[74],"density":[75],"additional":[77],"on-chip":[78],"memory.":[79],"However,":[80],"the":[81,96,104,171,189],"architecture,":[82],"programming":[83],"model,":[84],"performance-scaling":[86],"behavior":[87],"of":[88,95,106,174],"differ":[91],"fundamentally":[92],"from":[93],"those":[94],"programmable":[97,128,152,185],"logic,":[98],"making":[99],"direct":[100],"comparison":[101],"non-trivial":[102],"benefits":[105],"using":[107,188],"unclear.":[110],"This":[111],"work":[112],"addresses":[113],"how":[114],"when":[116,147,187],"neural":[119,176],"networks":[120,177],"should":[121],"be":[122],"implemented":[123],"versus":[127],"logic.":[129],"We":[130,155],"provide":[131],"systematic":[132],"architectural":[133],"characterization":[134],"micro-benchmarking":[136],"introduce":[138],"latency-adjusted":[140],"equivalence":[142],"(LARE)":[143],"metric":[144],"identifies":[146],"Engine":[149],"outperform":[151],"logic":[153,186],"designs.":[154],"further":[156],"propose":[157],"spatial":[158],"API-level":[160],"optimizations":[162],"tailored":[163],"low-latency":[165],"inference.":[167],"Finally,":[168],"we":[169],"demonstrate":[170],"successful":[172],"deployment":[173],"end-to-end":[175],"cannot":[182],"fit":[183],"hlsml":[190],"toolchain.":[191]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-23T00:00:00"}