An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing
Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained environments. The encoding of information to the hyperspace is the most important stage in HDC. At its heart are basis-hypervectors, responsible...
Uloženo v:
| Vydáno v: | 2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
09.07.2023
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained environments. The encoding of information to the hyperspace is the most important stage in HDC. At its heart are basis-hypervectors, responsible for representing atomic information. We present a detailed study on basis-hypervectors, leading to broad contributions to HDC: 1) an improvement for level-hypervectors, used to encode real numbers; 2) a method to learn from circular data, an important type of information never before addressed in HDC. Results indicate that these contributions lead to considerably more accurate models for classification and regression. |
|---|---|
| AbstractList | Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained environments. The encoding of information to the hyperspace is the most important stage in HDC. At its heart are basis-hypervectors, responsible for representing atomic information. We present a detailed study on basis-hypervectors, leading to broad contributions to HDC: 1) an improvement for level-hypervectors, used to encode real numbers; 2) a method to learn from circular data, an important type of information never before addressed in HDC. Results indicate that these contributions lead to considerably more accurate models for classification and regression. |
| Author | Heddes, Mike Givargis, Tony Nicolau, Alexandru Nunes, Igor |
| Author_xml | – sequence: 1 givenname: Igor surname: Nunes fullname: Nunes, Igor email: igord@uci.edu organization: UC Irvine,Department of Computer Science,Irvine,USA – sequence: 2 givenname: Mike surname: Heddes fullname: Heddes, Mike email: mheddes@uci.edu organization: UC Irvine,Department of Computer Science,Irvine,USA – sequence: 3 givenname: Tony surname: Givargis fullname: Givargis, Tony email: givargis@uci.edu organization: UC Irvine,Department of Computer Science,Irvine,USA – sequence: 4 givenname: Alexandru surname: Nicolau fullname: Nicolau, Alexandru email: nicolau@uci.edu organization: UC Irvine,Department of Computer Science,Irvine,USA |
| BookMark | eNo1kM9KAzEYxCMoqHXfQCQvsPXLn002x7ptrVDwouf67SYrgW5Skq3Yt3exygzMZeZ3mFtyGWJwhDwwmDMG5nG5aCpluJlz4GLOgEuthboghdGmFhUILmTNrkmRs29BQVVLUPKGfCwCXX2PLmQfAx0jfcLsc7k5HVz6ct0YU6Z9THTrMAUfPmmf4kAbn7rjHhNd4ojUB_rbt344c3BPmzgcjuM0uCNXPe6zK_5yRt7Xq7dmU25fn1-axbZEbmAssTVCaOwUtNLyvuqQ2UmdAKNMKwGdcUwra-sOBJvsaqyk0kxYzVvbixm5P3O9c253SH7AdNr9HyF-AJlWV0Q |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/DAC56929.2023.10247736 |
| 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 Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798350323481 |
| EndPage | 6 |
| ExternalDocumentID | 10247736 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IH ACM ALMA_UNASSIGNED_HOLDINGS CBEJK RIE RIO |
| ID | FETCH-LOGICAL-a290t-ab9337ac60b4d2f5ca1d1d1c30969b40ae9e176dd8c031031e8a546713d72bdf3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001073487300068&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 02:51:00 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a290t-ab9337ac60b4d2f5ca1d1d1c30969b40ae9e176dd8c031031e8a546713d72bdf3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10247736 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-July-9 |
| PublicationDateYYYYMMDD | 2023-07-09 |
| PublicationDate_xml | – month: 07 year: 2023 text: 2023-July-9 day: 09 |
| PublicationDecade | 2020 |
| PublicationTitle | 2023 60th ACM/IEEE Design Automation Conference (DAC) |
| PublicationTitleAbbrev | DAC |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssib060584064 |
| Score | 2.2722247 |
| Snippet | Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | basis-hypervectors circular data Computational modeling Data models Design automation Encoding Heart hyperdimensional computing information theory Machine learning |
| Title | An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing |
| URI | https://ieeexplore.ieee.org/document/10247736 |
| WOSCitedRecordID | wos001073487300068&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/eLvHCXMwlV07T8MwELZoxcAEiCDe8sDqEsdObI-lD3VAVQdA3YpfQVkSlKQVPx_bSUEMDMiLZflsya-zffd9B8C955jLbW4QJVojKrRCyqYcUaxoYrnlWSB7fn1iyyVfr8WqB6sHLIy1Njif2ZHPBlu-qfTWf5W5HZ5Qxkg2AAPGsg6stV883rznlBPtUcA4Fg_T8STNnPof-RDho73wrzAqQYvMj__Z_wmIfvB4cPWtaU7BgS3PwNu4hLPP4IBelbCt4KNsigYt3MOy3oW_-Aa6GynsGVTfoUeSwElRB89TOJWthEUJQ33jOf47fg7YxXlwAhF4mc-eJwvUx0tAMhFxi6QShDCps1hRk-Splti4pIl7pghFY2mFxSwzhmtPCEqw5TJ1ByUmhiXK5OQcDMuqtBcAulYk4TxnwmCaWsyFYlpqJlKtcyX5JYj88Gw-OkqMzX5krv4ovwZHfhKCn6u4AcO23tpbcKh3bdHUd2EivwBcIqDB |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT8MgFCc6TfSkxhm_5eCV2RZa4DjnlhnnssM0u02-anppTdst_vkC7TQePBguhPAI4es94P1-D4BbxzGXmlQjgpVChCuJpIkZIqEkkWGGJZ7s-XVCp1O2WPBZC1b3WBhjjHc-Mz2X9X_5ulAr91Rmd3hEKMXJNtiJCYmCBq61WT7ug8-qJ9LigMOA3z30B3FiDYCeCxLe24j_CqTi9cjo4J89OATdH0QenH3rmiOwZfJj8NbP4fDTu6AXOawLeC-qrEJje7Us1_41voLWJoUth-o7dFgSOMhK73sKH0QtYJZDX187lv-GoQM2kR6sQBe8jIbzwRi1EROQiHhQIyE5xlSoJJBER2msRKhtUtheVLgkgTDchDTRmilHCYpDw0Rsj8oQaxpJneIT0MmL3JwCaFsRmLGUch2S2ISMS6qEojxWKpWCnYGuG57lR0OKsdyMzPkf5Tdgbzx_niwnj9OnC7DvJsR7vfJL0KnLlbkCu2pdZ1V57Sf1C-ZfpAg |
| 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=2023+60th+ACM%2FIEEE+Design+Automation+Conference+%28DAC%29&rft.atitle=An+Extension+to+Basis-Hypervectors+for+Learning+from+Circular+Data+in+Hyperdimensional+Computing&rft.au=Nunes%2C+Igor&rft.au=Heddes%2C+Mike&rft.au=Givargis%2C+Tony&rft.au=Nicolau%2C+Alexandru&rft.date=2023-07-09&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FDAC56929.2023.10247736&rft.externalDocID=10247736 |