Campo DC | Valor | Lengua/Idioma |
dc.contributor.author | Yu, Xiaofan | - |
dc.contributor.author | Thomas, Anthony | - |
dc.contributor.author | Gomez Moreno, Ivannia | - |
dc.contributor.author | Gutierrez, Louis | - |
dc.contributor.author | Simunic Rosing, Tajana | - |
dc.date.accessioned | 2024-10-07T18:52:44Z | - |
dc.date.available | 2024-10-07T18:52:44Z | - |
dc.date.issued | 2024-03 | - |
dc.identifier.uri | https://repositorio.cetys.mx/handle/60000/1844 | - |
dc.description.abstract | On-device learning has emerged as a prevailing trend that avoids
the slow response time and costly communication of cloud-based
learning. The ability to learn continuously and indefinitely in a
changing environment, and with resource constraints, is critical
for real sensor deployments. However, existing designs are inadequate for practical scenarios with (i) streaming data input, (ii) lack
of supervision and (iii) limited on-board resources. In this paper,
we design and deploy the first on-device lifelong learning system
called LifeHD for general IoT applications with limited supervision. LifeHD is designed based on a novel neurally-inspired and
lightweight learning paradigm called Hyperdimensional Computing (HDC). We utilize a two-tier associative memory organization
to intelligently store and manage high-dimensional, low-precision
vectors, which represent the historical patterns as cluster centroids.
We additionally propose two variants of LifeHD to cope with scarce
labeled inputs and power constraints. We implement LifeHD on offthe-shelf edge platforms and perform extensive evaluations across
three scenarios. Our measurements show that LifeHD improves the
unsupervised clustering accuracy by up to 74.8% compared to the
state-of-the-art NN-based unsupervised lifelong learning baselines
with as much as 34.3x better energy efficiency. | es_ES |
dc.language.iso | en_US | es_ES |
dc.rights | Atribución-NoComercial-CompartirIgual 2.5 México | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/2.5/mx/ | * |
dc.subject | Edge Computing | es_ES |
dc.subject | Lifelong Learning | es_ES |
dc.subject | Hyperdimensional Computing | es_ES |
dc.title | Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing | es_ES |
dc.type | Article | es_ES |
dc.description.url | https://arxiv.org/abs/2403.04759 | es_ES |
dc.identifier.doi | https://doi.org/10.48550/arXiv.2403.04759 | - |
dc.subject.sede | Campus Tijuana | es_ES |
Aparece en las colecciones: | Ponencias
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