Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering

Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering

TinyML enables the deployment of Machine Learning (ML) models on resource-constrained devices, addressing a growing need for efficient, low-power AI solutions. However, significant challenges remain due to strict memory, processing, and energy limitations. This study introduces a novel method to opt...

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Bibliographic Details
Main Authors: Thommas K. S. Flores, Morsinaldo Medeiros, Marianne Silva, Daniel G. Costa, Ivanovitch Silva
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Embedded systems
post-training quantization
vector quantization
automotive sensors
pollution
Online Access:https://ieeexplore.ieee.org/document/10849357/
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