CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing

The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural networks. Prior research shows the effectiveness of HDC-based learning on less powerful systems such as edge computing devices. However, the...

Full description

Saved in:
Bibliographic Details
Published in:2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 775 - 780
Main Authors: Kim, Yeseong, Kim, Jiseung, Imani, Mohsen
Format: Conference Proceeding
Language:English
Published: IEEE 05.12.2021
Subjects:
Buildings
Computational modeling
Deep learning
Design automation
Edge Computing
Energy efficiency
Hyperdimensional Computing
Many-class classification
Performance evaluation
Training
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural networks. Prior research shows the effectiveness of HDC-based learning on less powerful systems such as edge computing devices. However, the many-class classification problem is beyond the focus of mainstream HDC research; the existing HDC would not provide sufficient quality and efficiency due to its coarse-grained training. In this paper, we propose an efficient many-class learning framework, called CascadeHD, which identifies latent high-dimensional patterns of many classes holistically while learning a hierarchical inference structure using a novel meta-learning algorithm for high efficiency. Our evaluation conducted on the NVIDIA Jetson device family shows that CascadeHD improves the accuracy for many-class classification by up to 18% while achieving 32% speedup compared to the existing HDC.
AbstractList The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural networks. Prior research shows the effectiveness of HDC-based learning on less powerful systems such as edge computing devices. However, the many-class classification problem is beyond the focus of mainstream HDC research; the existing HDC would not provide sufficient quality and efficiency due to its coarse-grained training. In this paper, we propose an efficient many-class learning framework, called CascadeHD, which identifies latent high-dimensional patterns of many classes holistically while learning a hierarchical inference structure using a novel meta-learning algorithm for high efficiency. Our evaluation conducted on the NVIDIA Jetson device family shows that CascadeHD improves the accuracy for many-class classification by up to 18% while achieving 32% speedup compared to the existing HDC.
Author Kim, Yeseong
Kim, Jiseung
Imani, Mohsen
Author_xml – sequence: 1
  givenname: Yeseong
  surname: Kim
  fullname: Kim, Yeseong
  email: yeseongkim@dgist.ac.kr
  organization: DGIST
– sequence: 2
  givenname: Jiseung
  surname: Kim
  fullname: Kim, Jiseung
  email: js980408@dgist.ac.kr
  organization: DGIST
– sequence: 3
  givenname: Mohsen
  surname: Imani
  fullname: Imani, Mohsen
  email: m.imani@uci.edu
  organization: UC Irvine
BookMark eNotj1FLwzAUhSMoqLO_QIT-gdabtGka30a3WaHig_N53KS3EmzT0lRk_96JezmHwwcHvlt26UdPjD1wSDkH_bhZV7wElacCBE-1LAuRyQsWaVXyopB5JlQO1ywKwRkoQJb5KW_Ye4XBYkv15inedp2zjvwSv6I_JlWPIcQN4eyd_4x3Mw70M85f8Uf42_Vxorl1A_ngRo99XI3D9L2c0B276rAPFJ17xfa77b6qk-bt-aVaNwkKrpZEGm6kNABKWJCKWyyxy9tOG6sktGhadXLRWHSAVouWhKLMgJVWUy5FtmL3_7eOiA7T7Aacj4ezefYLdz1R4g
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/DAC18074.2021.9586235
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 (IEL) (UW System Shared)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library (IEL) (UW System Shared)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781665432740
1665432748
EndPage 780
ExternalDocumentID 9586235
Genre orig-research
GrantInformation_xml – fundername: Semiconductor Research Corporation
  funderid: 10.13039/100000028
– fundername: Office of Naval Research
  funderid: 10.13039/100000006
GroupedDBID 6IE
6IH
ACM
ALMA_UNASSIGNED_HOLDINGS
CBEJK
RIE
RIO
ID FETCH-LOGICAL-a217t-5b1b55b0072c0571ca8af4df9bc750dabd72029a6f0ac92de27e3b0c5c9e4523
IEDL.DBID RIE
ISICitedReferencesCount 11
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000766079700130&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 02:28:29 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a217t-5b1b55b0072c0571ca8af4df9bc750dabd72029a6f0ac92de27e3b0c5c9e4523
PageCount 6
ParticipantIDs ieee_primary_9586235
PublicationCentury 2000
PublicationDate 2021-Dec.-5
PublicationDateYYYYMMDD 2021-12-05
PublicationDate_xml – month: 12
  year: 2021
  text: 2021-Dec.-5
  day: 05
PublicationDecade 2020
PublicationTitle 2021 58th ACM/IEEE Design Automation Conference (DAC)
PublicationTitleAbbrev DAC
PublicationYear 2021
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssib060584060
Score 2.2390108
Snippet The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural...
SourceID ieee
SourceType Publisher
StartPage 775
SubjectTerms Buildings
Computational modeling
Deep learning
Design automation
Edge Computing
Energy efficiency
Hyperdimensional Computing
Many-class classification
Performance evaluation
Training
Title CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing
URI https://ieeexplore.ieee.org/document/9586235
WOSCitedRecordID wos000766079700130&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/eLvHCXMwlV07T8MwED61FQMToBbxlgdG0sZ2HcdsqA91oapEh26Vn4iBFvXB7-fshCIkFrbIkRX5Yuu-8333HcB9n-ugKB4kUcTbqlCaTEctwsAYtabgoeBVswk5nZaLhZo14OFQC-O9T-Qz342PKZfv1nYfr8p6SiD-5qIJTSmLqlbre-_E7B76prwu0qG56g2fBjRKvWAQyGi3nvuriUryIeOT_339FDo_xXhkdnAzZ9Dwqza8DPQ2Mtsnw0cySioQOJc848nOUptLUuumvpLxN_uKJHYAmWDgiZviPRLXIwonVV8HfNWB-Xg0H0yyuj9CpjGQ2GXCUCOEieLfFmEXtbrUoe-CMhZxgNPGSVy60kXItVXMeSY9N7kVVvk-BqDn0FqtV_4CiAssl07GrCPiJ6oNK4XnzpU5s6V2_BLa0R7Lj0oBY1mb4urv4Ws4jiZPpA9xA63dZu9v4ch-7t62m7v0274AzWGZJw
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZKQYIJUIt444GRtLEdJzEb6kNBtFUlOnSr_EQMtKgPfj9nJy1CYmGLHFmRL7buO9933yF0nzDpBIGDxFN_W-VyFUmvRegoJVqlzKWsbDaRjUb5dCrGNfSwq4Wx1gbymW35x5DLNwu98VdlbcEBfzO-h_Z5ktC4rNba7h6f3wPvFFdlOiQW7e5Th3ixFwgDKWlVs3-1UQlepH_8v--foOZPOR4e7xzNKarZeQO9duTKc9uL7iPuBR0ImIuHcLaj0OgSV8qpb7i_5V_hwA_ABYSesC0-PHXd43BcdnaAV0006fcmnSKqOiREEkKJdcQVUZwrL_-tAXgRLXPpEuOE0oAEjFQmg6ULmbpYakGNpZllKtZcC5tACHqG6vPF3J4jbByNM5P5vCMgKCIVzbllxuQx1bk07AI1vD1mn6UGxqwyxeXfw3fosJgMB7PB8-jlCh158wcKCL9G9fVyY2_Qgf5av6-Wt-EXfgP0Vpxu
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=2021+58th+ACM%2FIEEE+Design+Automation+Conference+%28DAC%29&rft.atitle=CascadeHD%3A+Efficient+Many-Class+Learning+Framework+Using+Hyperdimensional+Computing&rft.au=Kim%2C+Yeseong&rft.au=Kim%2C+Jiseung&rft.au=Imani%2C+Mohsen&rft.date=2021-12-05&rft.pub=IEEE&rft.spage=775&rft.epage=780&rft_id=info:doi/10.1109%2FDAC18074.2021.9586235&rft.externalDocID=9586235