SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning
Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs) are well-known for their high accuracy due to the quality of their automatic feature extraction while brain-inspired h...
Uloženo v:
| Vydáno v: | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 9 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
Association on Computer Machinery
02.11.2020
|
| Témata: | |
| ISSN: | 1558-2434 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs) are well-known for their high accuracy due to the quality of their automatic feature extraction while brain-inspired hyperdimensional (HD) learning models are famous for their quick training, computational efficiency, and adaptability. This work presents a hybrid, synergic machine learning model that excels at all the said characteristics and is suitable for incremental, on-line learning on a chip. The proposed model comprises an NN and a classifier. The NN acts as a feature extractor and is specifically trained to work well with the classifier that employs the HD computing framework. This work also presents a parameterized hardware implementation of the said feature extraction and classification components while introducing a compiler that maps any arbitrary NN and/or classifier to the aforementioned hardware. The proposed hybrid machine learning model has the same level of accuracy (i.e. ±1%) as NNs while achieving at least 10% improvement in accuracy compared to HD learning models. Additionally, the end-to-end hardware realization of the hybrid model improves power efficiency by 1.60x compared to state-of-the-art, high-performance HD learning implementations while improving latency by 2.13x. These results have profound implications for the application of such synergic models in challenging cognitive tasks. |
|---|---|
| AbstractList | Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs) are well-known for their high accuracy due to the quality of their automatic feature extraction while brain-inspired hyperdimensional (HD) learning models are famous for their quick training, computational efficiency, and adaptability. This work presents a hybrid, synergic machine learning model that excels at all the said characteristics and is suitable for incremental, on-line learning on a chip. The proposed model comprises an NN and a classifier. The NN acts as a feature extractor and is specifically trained to work well with the classifier that employs the HD computing framework. This work also presents a parameterized hardware implementation of the said feature extraction and classification components while introducing a compiler that maps any arbitrary NN and/or classifier to the aforementioned hardware. The proposed hybrid machine learning model has the same level of accuracy (i.e. ±1%) as NNs while achieving at least 10% improvement in accuracy compared to HD learning models. Additionally, the end-to-end hardware realization of the hybrid model improves power efficiency by 1.60x compared to state-of-the-art, high-performance HD learning implementations while improving latency by 2.13x. These results have profound implications for the application of such synergic models in challenging cognitive tasks. |
| Author | Fayyazi, Arash Nazemi, Mahdi Esmaili, Amirhossein Pedram, Massoud |
| Author_xml | – sequence: 1 givenname: Mahdi surname: Nazemi fullname: Nazemi, Mahdi email: mnazemi@usc.edu organization: University of Southern California – sequence: 2 givenname: Amirhossein surname: Esmaili fullname: Esmaili, Amirhossein email: esmailid@usc.edu organization: University of Southern California – sequence: 3 givenname: Arash surname: Fayyazi fullname: Fayyazi, Arash email: fayyazi@usc.edu organization: University of Southern California – sequence: 4 givenname: Massoud surname: Pedram fullname: Pedram, Massoud email: pedram@usc.edu organization: University of Southern California |
| BookMark | eNo1jj1PwzAURQ0CiVI6M7DkD6T449mx2UrVUqQIBmCu7PglWLRO5aSC_HuCgOne4dyje0nOYhuRkGtG54yBvBVAqaB8LoBJZdQJmZlCM6UkcMFBnJIJk1LnY4ULMuu64CgABWm0npDmZYiYmlCVaFMMsbnLnvCY7G6M_rNNH_m97dBna7T9MWG2-uqTrfrQxqxuU7YJzftuyBdVNW56zDbDAZMPe4zdiIyWf-0VOa_trsPZX07J23r1utzk5fPD43JR5pZD0eeyNtpwA368WBkvpK6doUoaAOOUFg69ZKrwDrkrfkAuqTMMqFdFUXsUU3Lz6w2IuD2ksLdp2BoulWIgvgFuVFlp |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1145/3400302.3415696 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9781665423243 1665423242 |
| EISSN | 1558-2434 |
| EndPage | 9 |
| ExternalDocumentID | 9256614 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Science Foundation funderid: 10.13039/501100001809 |
| GroupedDBID | 6IE 6IF 6IH 6IL 6IN AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO FEDTE IEGSK IJVOP M43 OCL RIE RIL RIO |
| ID | FETCH-LOGICAL-a247t-5f989294d044c9d358fb90659449b683bed5167dbe2b79294250b9140d677fde3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 13 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000671087100089&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:28:38 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a247t-5f989294d044c9d358fb90659449b683bed5167dbe2b79294250b9140d677fde3 |
| PageCount | 9 |
| ParticipantIDs | ieee_primary_9256614 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-Nov.-2 |
| PublicationDateYYYYMMDD | 2020-11-02 |
| PublicationDate_xml | – month: 11 year: 2020 text: 2020-Nov.-2 day: 02 |
| PublicationDecade | 2020 |
| PublicationTitle | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design |
| PublicationTitleAbbrev | ICCAD |
| PublicationYear | 2020 |
| Publisher | Association on Computer Machinery |
| Publisher_xml | – name: Association on Computer Machinery |
| SSID | ssib044045988 ssj0020286 |
| Score | 2.2332265 |
| Snippet | Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example,... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | adaptability Adaptation models arbitrary NN Artificial neural networks automatic feature extraction brain-inspired hyperdimensional learning models classification components computational efficiency Computational modeling Data models end-to-end hardware realization feature extraction feature extractor HD computing framework HD learning models high-performance HD highly-accurate hyperdimensional learning hybrid machine learning model hybrid model Hyperdimensional Learning learning (artificial intelligence) machine learning models neural nets neural network-based feature extraction neural networks On-line Learning online learning Parallel Processing parameterized hardware implementation synergic machine learning model synergic models Training Training data training time |
| Title | SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning |
| URI | https://ieeexplore.ieee.org/document/9256614 |
| WOSCitedRecordID | wos000671087100089&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZKxQALjxbxlgdG0jaOY8dsgFp1QFUlQOpW1a-qUmlRSBD999y5oWVgYYoVRU50vujus-_7jpAbr5jJhMWCpziLuBYafinvI2MsS5T3ncwGdf0nORhko5Ea1sjthgvjnAvFZ66Fw3CWb5emxK2ytoL4LLBr9Y6UYs3V-vEdlLlLg_RWBbYgbopKyifmaTvh6M6slSBgCQr9214qIZT0Dv73EYekueXk0eEm2hyRmlsck_1fcoINMn1eIZNvZirR1OkdRe2NyRwuodg7eoCYZSmmfWXuaPeryNe8BgqpK8WSj_kqujemRP0I2geICu7zhiXumK_Tn2mb5LXXfXnsR1UjhWjCuCyi1KsM0iBuwUxG2STNvFZ4oMq50iJLtLNpLKTVjmmJD0JepBVALyuk9NYlJ6S-WC7cKVK8kctqJjHrWIBmYPGO9N7G8AruFE_PSANNNn5fa2WMK2ud_337guwxxK-4TcsuSb3IS3dFds1nMfvIr8MCfwPBt6aN |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEN4QNFEvPsD4dg8eLdDt9rHe1EAwIiERE26EfRESBFNbI__emaWABy-eummabTM7zcy3O983hNxYwVQSaSx48hOPy0jCL2Wtp5RmgbC2kWinrt-Ju91kMBC9Erldc2GMMa74zNRw6M7y9VzluFVWFxCfI-xavRVyzhpLttbKe1DoLnTiWwXcgsgZFWI-Pg_rAUeHZrUAIYvT6N90U3HBpLX_v884INUNK4_21vHmkJTM7Ijs_RIUrJDx6wK5fBNVyKaO7yiqb4ymcHHl3t4DRC1NMfHLU0Ob31m6ZDZQSF4pFn1MF969UjkqSNA2gFRwoHcscseMna6mrZK3VrP_2PaKVgreiPE480IrEkiEuAYzKaGDMLFS4JEq50JGSSCNDv0o1tIwGeODkBlJAeBLR3FstQmOSXk2n5kTJHkjm1WNfNbQAM7A4o3YWu3DK7gRPDwlFTTZ8GOpljEsrHX29-1rstPuv3SGnafu8znZZYhmcdOWXZBylubmkmyrr2zymV65xf4Bmo2p1A |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Digest+of+technical+papers+-+IEEE%2FACM+International+Conference+on+Computer-Aided+Design&rft.atitle=SynergicLearning%3A+Neural+Network-Based+Feature+Extraction+for+Highly-Accurate+Hyperdimensional+Learning&rft.au=Nazemi%2C+Mahdi&rft.au=Esmaili%2C+Amirhossein&rft.au=Fayyazi%2C+Arash&rft.au=Pedram%2C+Massoud&rft.date=2020-11-02&rft.pub=Association+on+Computer+Machinery&rft.eissn=1558-2434&rft.spage=1&rft.epage=9&rft_id=info:doi/10.1145%2F3400302.3415696&rft.externalDocID=9256614 |