{"id":"https://openalex.org/W3188841390","doi":"https://doi.org/10.1109/islped52811.2021.9502470","title":"Co-Designing Hardware and Models for Efficient On-Device ML Inference","display_name":"Co-Designing Hardware and Models for Efficient On-Device ML Inference","publication_year":2021,"publication_date":"2021-07-26","ids":{"openalex":"https://openalex.org/W3188841390","doi":"https://doi.org/10.1109/islped52811.2021.9502470","mag":"3188841390"},"language":"en","primary_location":{"id":"doi:10.1109/islped52811.2021.9502470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped52811.2021.9502470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","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/A5090422974","display_name":"Matthew Mattina","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156213","display_name":"American Rock Mechanics Association","ror":"https://ror.org/05vfrxy92","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156213"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Matthew Mattina","raw_affiliation_strings":["Arm Machine Learning Research Lab"],"affiliations":[{"raw_affiliation_string":"Arm Machine Learning Research Lab","institution_ids":["https://openalex.org/I4210156213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5090422974"],"corresponding_institution_ids":["https://openalex.org/I4210156213"],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54058966,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9857000112533569,"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":0.9857000112533569,"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":0.9789000153541565,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9577000141143799,"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":0.7498148083686829},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7085008025169373},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6987327337265015},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5561114549636841},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5525974631309509},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5418754816055298},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5272154808044434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46070802211761475},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4485350251197815},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4355087876319885},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.4234354496002197},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.40799593925476074},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3944125771522522},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3398154377937317},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3198709487915039},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.25627103447914124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7498148083686829},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7085008025169373},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6987327337265015},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5561114549636841},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5525974631309509},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5418754816055298},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5272154808044434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46070802211761475},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4485350251197815},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4355087876319885},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.4234354496002197},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.40799593925476074},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3944125771522522},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3398154377937317},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3198709487915039},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.25627103447914124},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/islped52811.2021.9502470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped52811.2021.9502470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"}],"best_oa_location":null,"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/W3098411449","https://openalex.org/W3177201542","https://openalex.org/W4285299124","https://openalex.org/W3104123609","https://openalex.org/W4293261943","https://openalex.org/W3103213585","https://openalex.org/W2949493108","https://openalex.org/W3081330725","https://openalex.org/W4206570089","https://openalex.org/W2974437627"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"inference":[2],"at":[3,76],"the":[4,11,46],"edge":[5],"continues":[6],"to":[7,33,82,89],"deliver":[8],"state":[9],"of":[10],"art":[12],"results":[13],"on":[14,45,91],"real-world":[15],"applications":[16],"involving":[17],"images,":[18],"video,":[19],"speech,":[20],"and":[21,37,55,65,80],"human":[22],"activity.":[23],"The":[24],"workhorse":[25],"behind":[26],"these":[27],"advances\u2014increasingly":[28],"complex":[29,87],"neural":[30,78],"network":[31,63,67],"models\u2014continue":[32],"grow":[34],"in":[35],"size":[36],"computational":[38],"requirements.":[39],"These":[40],"advances":[41],"place":[42],"significant":[43],"demand":[44],"energy-constrained":[47],"hardware":[48,81,93],"platforms":[49],"responsible":[50],"for":[51],"executing":[52],"such":[53],"models":[54,88],"are":[56],"driving":[57],"trends":[58],"like":[59],"low-precision":[60],"number":[61],"formats,":[62],"pruning,":[64],"complexity-reducing":[66],"transforms.":[68],"This":[69],"talk":[70],"will":[71],"discuss":[72],"emerging":[73],"research":[74],"aimed":[75],"co-designing":[77],"networks":[79],"enable":[83],"even":[84],"larger,":[85],"more":[86],"operate":[90],"highly-constrained":[92],"platforms.":[94]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
