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...
Saved in:
| Published in: | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 1 - 9 |
|---|---|
| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
Association on Computer Machinery
02.11.2020
|
| Subjects: | |
| ISSN: | 1558-2434 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 Electronic Library (IEL) IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore 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 | 2.233327 |
| 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/eLvHCXMwlV07T8MwELZKxQALjxbxlgdG0taOHzEboFadqkqA1K1q7EtVqbQoJIj-e3xu2jKwMCWKIis6X3zfZ999R8idFiANAxMJ55dAoRjzv5RMIg91FTCALAYbmk3owSAZjcywRu63tTAAEJLPoIW34SzfLW2JW2Vt4-Ozwq7Ve1qrda3WxndQ5k4G6a2KbPm4qSopHyZkOxbozrwVI2EJCv27XiohlPSO_vcRx6S5q8mjw220OSE1WJySw19ygg0yfVlhJd_MVqKp0weK2huTub-EZO_oyccsRxH2lTnQ7neRr-saqIeuFFM-5qvo0doS9SNo31NU7z7vmOKOeJ1uhm2St1739bkfVY0UogkXuohkZhIPg4TzZrLGxTLJUoMHqkKYVCVxCk4ypV0KPNX4osdFqfHUyymtMwfxGakvlgs4J9R6OMl51lGOWSGtnsQTDspqz4J4BwS7IA002fhjrZUxrqx1-ffjK3LAkb_iNi2_JvUiL-GG7NuvYvaZ34YJ_gEsSKXu |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEN4QNFEvPsD4dg8eLdDtPrre1EAwIiERE26E7k4JCYKprZF_704p4MGLpzZNs2lmpzvftzvzDSE3ioPQPmiPW7cEcun77pcSoeegrgQfIA7A5M0mVLcbDga6VyK361oYAMiTz6CGt_lZvp2bDLfK6trFZ4ldq7cE56yxrNZaeQ8K3YlcfKugWy5yykLMx-eiHnB0aFYLkLLkGv2bbip5MGnt_-8zDkh1U5VHe-t4c0hKMDsie78EBStk_LrAWr6JKWRTx3cU1TdGU3fJ0729Bxe1LEXglyVAm99psqxsoA68Ukz6mC68e2MyVJCgbUdSnQO9Y5I7Ina6GrZK3lrN_mPbK1opeCPGVeqJWIcOCHHrzGS0DUQYRxqPVDnXkQyDCKzwpbIRsEjhiw4ZRdqRLyuVii0Ex6Q8m8_ghFDjACVjcUNa33Bh1CgYMZBGOR7EGsD9U1JBkw0_lmoZw8JaZ38_viY77f5LZ9h56j6fk12GbBY3bdkFKadJBpdk23ylk8_kKp_sH_wnqTU |
| 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 |